From ai94@fermat.une.edu.au Fri Jan 21 16:44:01 EST 1994 Article: 20312 of comp.ai Xref: glinda.oz.cs.cmu.edu comp.ai:20312 comp.ai.edu:1548 comp.ai.neural-nets:14348 comp.ai.philosophy:16488 comp.ai.genetic:2082 comp.ai.fuzzy:1707 comp.ai.nat-lang:1094 Path: honeydew.srv.cs.cmu.edu!bb3.andrew.cmu.edu!news.sei.cmu.edu!cis.ohio-state.edu!math.ohio-state.edu!sdd.hp.com!sgiblab!munnari.oz.au!newshost.anu.edu.au!sserve!usage!metro!grivel!fermat.une.edu.au!ai94 From: ai94@fermat.une.edu.au (Artificial Intelligence Conference 1994) Newsgroups: aus.ai,aus.computers.ai,comp.ai,comp.ai.edu,comp.ai.neural-nets,comp.ai.philosophy,comp.ai.genetic,comp.ai.fuzzy,comp.ai.nat-lang,une.general Subject: CFP: Australian Joint Conference on Artificial Intelligence (AI'94) Keywords: conference, ai, cfp Message-ID: <3086@grivel.une.edu.au> Date: 19 Jan 94 01:37:07 GMT Sender: usenet@grivel.une.edu.au Followup-To: poster Lines: 164 Nntp-Posting-Host: fermat.une.edu.au ========= F I R S T ========= C A L L F O R P A P E R S Seventh Australian Joint Conference on Artificial Intelligence (AI'94) "Sowing the Seeds for the Future" 21 - 25 November 1994 Proudly sponsored by Microsoft Institute (principal sponsor), IBM, Sun Microsystems, Australian Computer Society, and Department of Mathematics, Statistics, and Computing Science (UNE). Hosted by Department of Mathematics, Statistics, and Computing Science The University of New England Armidale, N.S.W., 2351 AUSTRALIA AI'94 is the Seventh Australian Joint Conference on Artificial Intelligence. AI'94 is conducted under the auspices of the Australian Computer Society's National Committee for Artificial Intelligence and Expert Systems. The theme of the conference is "Sowing the Seeds for the Future", which reflects the nature of research in Artificial Intelligence. The goal of the conference is to promote research in artificial intelligence (AI) and scientific interchange among AI researchers and practitioners. AI'94 will be hosted by The Department of Mathematics, Statistics, and Computing Science at The University of New England, between Monday 21st November to Friday 25th November 1994. PROGRAM COMMITTEE CO-CHAIR ORGANISING COMMITTEE Dr. Chengqi Zhang (co-chair) Dr. Dickson Lukose (chair) Prof. John Debenham (co-chair) Mr. Allan Williams (secretary) Dr. Simant Dube (treasurer) Mr. Neil Dunstan Ms. Gabrielle Aldridge We invite authors to submit papers describing both experimental and theoretical results from all stages of AI research. In particular, we encourage submission of papers that describe innovative concepts, techniques, perspectives, or observations that are not yet supported by mature results. Such submissions must include substantial analysis of the ideas, the technology needed to realise them, and their potential impact. Papers describing applied AI are particularly solicited. In addition, because of the essential interdisciplinary nature of AI and the need to maintain effective communication across sub-specialties, we encourage authors to position and motivate their work in the larger context of the general AI community. Topics of interest include, but are not limited to: Machine Learning Knowledge Acquisition Natural Language Processing Natural Language Understanding Hybrid Systems Genetic Algorithms Evolutionary Programming Knowledge Based Systems Knowledge Representation Qualitative Reasoning Automated Reasoning Planning and Scheduling Cognitive Modelling Robotics Vision Distributed Artificial Intelligence Neural Network Image Analysis Authors are invited to submit complete, original papers in the format specified below, reflecting their current research results. All submitted papers will be refereed for quality and originality. The program committee reserves the right to accept submissions as either technical or poster presentation paper. Authors must submit five (5) copies of the completed paper to the AI'94 Conference Secretary at the following address by 15th. June 1994. AI'94 Conference Secretary Department of Mathematics, Statistics, and Computing Science The University of New England Armidale, N.S.W., 2351 AUSTRALIA All five (5) copies of the submitted paper must be clearly legible. Neither computer files nor fax submission are acceptable. Papers received after the 15th. June 1994 will be returned unopened. Notification of receipt will be mailed to the first author (or designated author) soon after receipt. PAPER FORMAT FOR REVIEW All five copies of the submissions must be printed on 8 1/2" x 11" or A4 paper using 12 point type (10 characters per inch for typewriters or 12 point LaTeX article-style). Double-sided printing is strongly encouraged. The body of submitted papers must be at most 8 pages, including figures, tables, diagrams, and bibliography, but excluding the title page. Papers exceeding the specified length or formatting requirements are subject to rejection without review. Each copy of the paper must have a title page (separate from the body of the paper) containing the title of the paper, the names and addresses of all authors, telephone number, fax number and electronic mail address, a short (less than 200 word) abstract, and a descriptive content area or areas. The body of the paper should have a copy of the title and a page number on each page. To facilitate the reviewing process, authors are requested to select appropriate content areas from the list below. Authors are invited to add additional content area descriptors to their title page as needed. Artificial Life, Automated Reasoning, Behaviour-Based Control, Belief Revision, Case-Based Reasoning, Cognitive Modelling, Common Sense Reasoning, Communication and Cooperation, Constraint-Based Reasoning, Computer-Aided Education, Connectionist Models, Corpus-Based Language Analysis, Deduction, Diagnosis, Discourse Analysis, Distributed Problem Solving, Expert Systems, Geometrical Reasoning, Information Extraction, Knowledge Acquisition, Knowledge Representation, Knowledge Sharing Technology, Large Scale Knowledge Engineering, Learning/Adaptation, Machine Learning, Machine Translation, Mathematical Foundations, Multi-Agent Planning, Natural Language Processing, Neural Networks, Nonmonotonic Reasoning, Perception, Planning, Probabilistic Reasoning, Qualitative Reasoning, Reasoning about Action, Reasoning about Physical Systems, Reactivity, Robot Navigation, Robotics, Rule-Based Reasoning, Scheduling, Search, Sensor Interpretation, Sensory Fusion/Fission, Simulation, Situated Cognition, Spatial Reasoning, Speech Recognition, System Architectures, Temporal Reasoning, Terminological Reasoning, Theorem Proving, Truth Maintenance, User Interfaces, Virtual Reality, Vision, 3-D Model Acquisition. Each paper will be carefully reviewed. Questions that will appear on the review form have been reproduced below. Authors are advised to bear these questions in mind while writing their papers: How important is the work reported? Does it attack an important/difficult problem or a peripheral/simple one? Does the approach offered advance the state of the art? Has this or similar work been previously reported? Are the problems and approaches completely new? Is this a novel combination of familiar techniques? Does the paper point out differences from related research? Is it re-inventing the wheel using new terminology? Is the paper technically sound? Does it carefully evaluate the strengths and limitations of its contribution? How are its claims backed up? Is the paper clearly written? Does it motivate the research? Does it describe the inputs, outputs and basic algorithms employed? Does the paper describe previous work? Are the results described and evaluated? Is the paper organised in a logical fashion? IMPORTANT DATES Deadline for paper submission : 15th. June 1994 Notification of acceptance : 31st. July 1994 Camera Ready Copy : 22nd. August 1994 FURTHER INFORMATION All enquires regarding AI'94 should be directed to the following address: AI'94 Conference Secretary Department of Mathematics, Statistics, and Computing Science The University of New England Armidale, N.S.W., 2351 AUSTRALIA E-mail: ai94@fermat.une.edu.au You may e-mail the following address with the Subject Heading "help" to obtain details on AI'94, UNE, and Armidale. ai94-info@fermat.une.edu.au ai94-info mail server has been established to enable electronic request for information regarding AI'94 Conference. Article 5479 of news.announce.conferences: Xref: glinda.oz.cs.cmu.edu news.announce.conferences:5479 Newsgroups: news.announce.conferences Path: honeydew.srv.cs.cmu.edu!bb3.andrew.cmu.edu!news.sei.cmu.edu!cis.ohio-state.edu!magnus.acs.ohio-state.edu!usenet.ins.cwru.edu!howland.reston.ans.net!pipex!uunet!sparky!rick From: ai94@fermat.une.edu.au (Artificial Intelligence Conference 1994) Subject: CFP: Seventh Australian Joint Conference on Artificial Intelligence Message-ID: <1994Jan24.202745.28930@sparky.sterling.com> Followup-To: poster Keywords: AI94, Conference Sender: rick@sparky.sterling.com (Richard Ohnemus) Organization: Sterling Software Date: Mon, 24 Jan 1994 20:27:45 GMT Approved: rick@sparky.sterling.com Expires: Thu, 16 Jun 1994 08:00:00 GMT Lines: 164 X-Md4-Signature: 803c867af373530cd90f814802155289 ========= F I R S T ========= C A L L F O R P A P E R S Seventh Australian Joint Conference on Artificial Intelligence (AI'94) "Sowing the Seeds for the Future" 21 - 25 November 1994 Proudly sponsored by Microsoft Institute (principal sponsor), IBM, Sun Microsystems, Australian Computer Society, and Department of Mathematics, Statistics, and Computing Science (UNE). Hosted by Department of Mathematics, Statistics, and Computing Science The University of New England Armidale, N.S.W., 2351 AUSTRALIA AI'94 is the Seventh Australian Joint Conference on Artificial Intelligence. AI'94 is conducted under the auspices of the Australian Computer Society's National Committee for Artificial Intelligence and Expert Systems. The theme of the conference is "Sowing the Seeds for the Future", which reflects the nature of research in Artificial Intelligence. The goal of the conference is to promote research in artificial intelligence (AI) and scientific interchange among AI researchers and practitioners. AI'94 will be hosted by The Department of Mathematics, Statistics, and Computing Science at The University of New England, between Monday 21st November to Friday 25th November 1994. PROGRAM COMMITTEE CO-CHAIR ORGANISING COMMITTEE Dr. Chengqi Zhang (co-chair) Dr. Dickson Lukose (chair) Prof. John Debenham (co-chair) Mr. Allan Williams (secretary) Dr. Simant Dube (treasurer) Mr. Neil Dunstan Ms. Gabrielle Aldridge We invite authors to submit papers describing both experimental and theoretical results from all stages of AI research. In particular, we encourage submission of papers that describe innovative concepts, techniques, perspectives, or observations that are not yet supported by mature results. Such submissions must include substantial analysis of the ideas, the technology needed to realise them, and their potential impact. Papers describing applied AI are particularly solicited. In addition, because of the essential interdisciplinary nature of AI and the need to maintain effective communication across sub-specialties, we encourage authors to position and motivate their work in the larger context of the general AI community. Topics of interest include, but are not limited to: Machine Learning Knowledge Acquisition Natural Language Processing Natural Language Understanding Hybrid Systems Genetic Algorithms Evolutionary Programming Knowledge Based Systems Knowledge Representation Qualitative Reasoning Automated Reasoning Planning and Scheduling Cognitive Modelling Robotics Vision Distributed Artificial Intelligence Neural Network Image Analysis Authors are invited to submit complete, original papers in the format specified below, reflecting their current research results. All submitted papers will be refereed for quality and originality. The program committee reserves the right to accept submissions as either technical or poster presentation paper. Authors must submit five (5) copies of the completed paper to the AI'94 Conference Secretary at the following address by 15th. June 1994. AI'94 Conference Secretary Department of Mathematics, Statistics, and Computing Science The University of New England Armidale, N.S.W., 2351 AUSTRALIA All five (5) copies of the submitted paper must be clearly legible. Neither computer files nor fax submission are acceptable. Papers received after the 15th. June 1994 will be returned unopened. Notification of receipt will be mailed to the first author (or designated author) soon after receipt. PAPER FORMAT FOR REVIEW All five copies of the submissions must be printed on 8 1/2" x 11" or A4 paper using 12 point type (10 characters per inch for typewriters or 12 point LaTeX article-style). Double-sided printing is strongly encouraged. The body of submitted papers must be at most 8 pages, including figures, tables, diagrams, and bibliography, but excluding the title page. Papers exceeding the specified length or formatting requirements are subject to rejection without review. Each copy of the paper must have a title page (separate from the body of the paper) containing the title of the paper, the names and addresses of all authors, telephone number, fax number and electronic mail address, a short (less than 200 word) abstract, and a descriptive content area or areas. The body of the paper should have a copy of the title and a page number on each page. To facilitate the reviewing process, authors are requested to select appropriate content areas from the list below. Authors are invited to add additional content area descriptors to their title page as needed. Artificial Life, Automated Reasoning, Behaviour-Based Control, Belief Revision, Case-Based Reasoning, Cognitive Modelling, Common Sense Reasoning, Communication and Cooperation, Constraint-Based Reasoning, Computer-Aided Education, Connectionist Models, Corpus-Based Language Analysis, Deduction, Diagnosis, Discourse Analysis, Distributed Problem Solving, Expert Systems, Geometrical Reasoning, Information Extraction, Knowledge Acquisition, Knowledge Representation, Knowledge Sharing Technology, Large Scale Knowledge Engineering, Learning/Adaptation, Machine Learning, Machine Translation, Mathematical Foundations, Multi-Agent Planning, Natural Language Processing, Neural Networks, Nonmonotonic Reasoning, Perception, Planning, Probabilistic Reasoning, Qualitative Reasoning, Reasoning about Action, Reasoning about Physical Systems, Reactivity, Robot Navigation, Robotics, Rule-Based Reasoning, Scheduling, Search, Sensor Interpretation, Sensory Fusion/Fission, Simulation, Situated Cognition, Spatial Reasoning, Speech Recognition, System Architectures, Temporal Reasoning, Terminological Reasoning, Theorem Proving, Truth Maintenance, User Interfaces, Virtual Reality, Vision, 3-D Model Acquisition. Each paper will be carefully reviewed. Questions that will appear on the review form have been reproduced below. Authors are advised to bear these questions in mind while writing their papers: How important is the work reported? Does it attack an important/difficult problem or a peripheral/simple one? Does the approach offered advance the state of the art? Has this or similar work been previously reported? Are the problems and approaches completely new? Is this a novel combination of familiar techniques? Does the paper point out differences from related research? Is it re-inventing the wheel using new terminology? Is the paper technically sound? Does it carefully evaluate the strengths and limitations of its contribution? How are its claims backed up? Is the paper clearly written? Does it motivate the research? Does it describe the inputs, outputs and basic algorithms employed? Does the paper describe previous work? Are the results described and evaluated? Is the paper organised in a logical fashion? IMPORTANT DATES Deadline for paper submission : 15th. June 1994 Notification of acceptance : 31st. July 1994 Camera Ready Copy : 22nd. August 1994 FURTHER INFORMATION All enquires regarding AI'94 should be directed to the following address: AI'94 Conference Secretary Department of Mathematics, Statistics, and Computing Science The University of New England Armidale, N.S.W., 2351 AUSTRALIA E-mail: ai94@fermat.une.edu.au You may e-mail the following address with the Subject Heading "help" to obtain details on AI'94, UNE, and Armidale. ai94-info@fermat.une.edu.au ai94-info mail server has been established to enable electronic request for information regarding AI'94 Conference. Article 21179 of comp.ai: Xref: glinda.oz.cs.cmu.edu comp.ai:21179 Path: honeydew.srv.cs.cmu.edu!fs7.ece.cmu.edu!europa.eng.gtefsd.com!howland.reston.ans.net!vixen.cso.uiuc.edu!uwm.edu!msuinfo!harbinger.cc.monash.edu.au!aggedor.rmit.EDU.AU!goanna.cs.rmit.oz.au!not-for-mail From: ged@goanna.cs.rmit.oz.au (Gerard Ellis) Newsgroups: comp.ai,aus.computers.ai Subject: Australian Conceptual Graphs Workshop: Call for Papers Date: 18 Mar 1994 13:19:44 +1000 Organization: Comp Sci, RMIT, Melbourne, Australia Lines: 96 Message-ID: <2mb6kg$mbo@goanna.cs.rmit.oz.au> NNTP-Posting-Host: goanna.cs.rmit.oz.au NNTP-Posting-User: ged Summary: call for papers Keywords: conceptual graphs, workshop, logic, knowledge representation, ai 1st Australian Conceptual Structures Workshop in association with the Seventh Australian Joint Conference on Artificial Intelligence (AI'94) November 22, 1994 University of New England, Armidale, New South Wales THEME Conceptual graphs are a logic-based formalism for knowledge representation based on the existential graphs of Charles S. Peirce and semantic networks. 1994 marks the tenth anniversary of their use. During this time conceptual structures have been widely used as a semantic representation for natural language and as a graphic system of logic for expert systems, theorem provers, and database design. Significant gains have been made in the storage and retrieval of DBMS information coupled with knowledge-based system problem solving capability. Researchers have developed a sizable software base and continue to build upon it. Successful implementations include: rule-based systems, database systems, knowledge-based systems, knowledge engineering tools, enterprise modeling, management information systems, conceptual information retrieval, medical informatics and natural language applications, among others. Conceptual graphs are being proposed as a basis for the normative language for conceptual schemas by the ANSI X3H4 Committee on Information Resource Dictionary Systems. Conceptual graphs are also proposed with Knowledge Interchange Format (KIF) as the standard for knowledge interchange between computer systems. We encourage the submission of position papers concerning conceptualization, formation and modeling using conceptual graphs. TOPICS and ISSUES Papers are invited on any aspect of concept analysis, representation, or manipulation involving conceptual graphs. The following topics are of particular interest but others, concerned with conceptual graphs, will be welcome as well. Theory Technical developments Applications Natural language understanding Graph notation Theorem Proving Ontology INVITED TALK John F. Sowa, SUNY at Binghamton (USA) "Knowledge Representation: Logical, Philosophical, and Computational Foundations" Norman Foo, Sydney Univ. "A Framework for Ontology Revision" PROGRAM COMMITTEE Peter Creasy University of Queensland Peter Eklund Adelaide University (Co-chair) Gerard Ellis University of Queensland (Co-chair) Norman Foo University of Sydney Dickson Lukose University of New England John Sowa SUNY at Binghamton (USA) Eric Tsui Continuum Australia Ltd. IMPORTANT DATES Submission Deadline June 30, 1994. Notification of Acceptance August 30, 1994. Camera-ready copy October 15, 1994. Submissions and enquiries to Gerard Ellis Computer Science Dept, Royal Melbourne Institute of Technology GPO Box 2476V, Melbourne, Victoria, 3001. Ph:61-3-660-2544 FAX:61-3-662-1617 Email: ged@cs.rmit.edu.au -- Gerard Ellis ged@cs.rmit.edu.au ph:61-3-660-2544 FAX:61-3-662-1617 Rm:12.10.09 Computer Science Dept, Royal Melbourne Institute of Technology GPO Box 2476V, Melbourne, Victoria, 3001, AUSTRALIA Article 21391 of comp.ai: Xref: glinda.oz.cs.cmu.edu comp.ai:21391 comp.ai.edu:1709 comp.ai.neural-nets:15644 comp.ai.philosophy:17483 comp.ai.genetic:2567 comp.ai.fuzzy:2057 comp.ai.nat-lang:1426 Path: honeydew.srv.cs.cmu.edu!bb3.andrew.cmu.edu!news.sei.cmu.edu!cis.ohio-state.edu!math.ohio-state.edu!howland.reston.ans.net!agate!msuinfo!harbinger.cc.monash.edu.au!newshost.anu.edu.au!sserve!usage!metro!grivel!fermat.une.edu.au!ai94 From: ai94@fermat.une.edu.au (Artificial Intelligence Conference 1994) Newsgroups: aus.ai,aus.computers.ai,comp.ai,comp.ai.edu,comp.ai.neural-nets,comp.ai.philosophy,comp.ai.genetic,comp.ai.fuzzy,comp.ai.nat-lang,une.general Subject: Second Call For Papers AI94 Message-ID: <4111@grivel.une.edu.au> Date: 28 Mar 94 12:19:22 GMT Sender: usenet@grivel.une.edu.au Followup-To: poster Lines: 216 Nntp-Posting-Host: fermat.une.edu.au S E C O N D C A L L F O R P A P E R S Seventh Australian Joint Conference on Artificial Intelligence (AI'94) "Sowing the Seeds for the Future" 21 - 25 November 1994 Proudly sponsored by Microsoft Institute (principal sponsor), IBM, Sun Microsystems, Australian Computer Society, and Department of Mathematics, Statistics, and Computing Science (UNE). Hosted by Department of Mathematics, Statistics, and Computing Science The University of New England,Armidale, N.S.W., 2351, AUSTRALIA AI'94 is the Seventh Australian Joint Conference on Artificial Intelligence. The theme of the conference is "Sowing the Seeds for the Future", which reflects the nature of research in Artificial Intelligence. The goal of the conference is to promote research in artificial intelligence (AI) and scientific interchange among AI researchers and practitioners. AI'94 will be hosted by The Department of Mathematics, Statistics, and Computing Science at The University of New England, between Monday 21st November and Friday 25th November 1994. The conference programme will consist of formal tutorials and workshops on the Monday and Tuesday, a Postgraduate session on Tuesday, and technical paper presentation sessions from Wednesday 23rd to Friday 25th of November. In addition to these sessions there will be three Keynote addresses from renowned international speakers. Wednesday, 23rd November : Professor Wolfgang Wahlster, German Research Center for AI (DFKI) Topic of address : Intellimedia: Planning Language, Graphics and Layout for Adaptive Information Presentation Wolfgang Wahlster is a Professor of Artificial Intelligence in the Department of Computer Science at the University of Saarbruecken, Germany where he currently serves as a Scientific Director of the German Research Center for Artificial Intelligence (DFKI). Since 1975 he has been the principal investigator in various language projects, including HAM-ANS, WISBER, SC, XTRA, VITRA and WIP. He has published over 100 technical papers on natural language processing. His current research includes intelligent multimodal interfaces, user modeling, natural language scene description, intelligent help systems, and deductive plan recognition and generation. Prof. Wahlster is on the editorial boards of various international journals and book series such as Artificial Intelligence, Applied Artificial Intelligence, User Modeling and User-adapted Interaction, Symbolic Computation and the MIT-ACL series. He is a AAAI Fellow and a recipient of the Fritz Winter Award, one of the most prestigious awards for engineering sciences in Germany, for his research on cooperative user interfaces. Prof. Wahlster served as the Conference Chair for IJCAI-93 in Chambery and the Chair of the Board of Trustees of IJCAII from 1991 -1993. Thursday, 24th November : Professor Katia Sycara, Carnegie Mellon University Topic of address : The Present and Future of Distributed Artificial Intelligence Katia Sycara is a Research Scientist in the School of Computer Science at Carnegie Mellon University. She is also Director of the Enterprise Integration Laboratory. She is directing and conducting research aimed at developing decision support systems for integrating organisational decision making. Her doctoral research contributed to the definition of the case-based reasoning paradigm. She has been Principal Investigator of various government and industry funded research (e.g. distributed scheduling, concurrent engineering, enterprise integration, case-based Engineering design, crisis action planning). Dr. Sycara is the author of a book on manufacturing and over 70 technical papers dealing with negotiation, distributed problem solving, case-based reasoning, integration of case-based reasoning with other problem solving methods, and constraint-based reasoning. She is the Area Editor for AI and Management Science for the journal "Group Decision and Negotiation" and on the editorial board of "AI in Engineering" and "Concurrent Engineering: Research and Applications". She is a member of AAAI, ACM, IEEE, and the Institute for Management Science (TIMS). Friday, 25th November : Professor John F. Sowa, State University of New York - Binghamton Topic of Address : Sharing and Integrating Knowledge Bases John F. Sowa is the author of the book Conceptual Structures, which in the past ten years has led to a world-wide movement of people who are using, implementing, and extending the theory of conceptual graphs. He had been working at IBM for 30 years on various aspects of computer systems design and development, especially artificial intelligence and computational linguistics. Now, he is teaching, writing, and working on standards for conceptual schemas with the American National Standards Institute (ANSI) and the International Standards Organization (ISO). PROGRAM COMMITTEE Dr. Chengqi Zhang (co-chair); UNE Dr. Dickson Lukose; UNE Prof. John Debenham (co-chair); UTS Dr. Anand Rao; AAII A/Prof. Mike Brooks; Adelaide A/Prof. Claude Sammut; UNSW Dr. Jennie Clothier; DSTO A/Prof. Liz Sonenberg; Melbourne Dr. Robert Dale; Microsoft Prof. Rodney Topor; Griffith A/Prof. Wee Leng Goh; NTU, Singapore Dr. Wayne Wobcke; Sydney Mr. Andy Horsfall; Fujitsu Dr. Xindong Wu; James Cook Prof. Ray Jarvis; Monash Dr. Xin Yao; ADFA Dr. Chris Leckie; TRL Dr. Waikiang Yeap; Otago, N.Z. Dr. Craig Lindley; CSIRO ORGANISING COMMITTEE Dr. Dickson Lukose (chair) Dr. Simant Dube Mr. Prakash Bhandari Mr. Allan Williams (secretary) Dr. Gregory Zevin Ms. Gabrielle Aldridge We invite authors to submit papers describing both experimental and theoretical results from all stages of AI research. We encourage submission of papers that describe innovative concepts, techniques, perspectives, or observations that are not yet supported by mature results. Such submissions must include substantial analysis of the ideas, the technology needed to realise them, and their potential impact. Papers describing applied AI are particularly solicited. Topics of interest include, but are not limited to: Machine Learning Distributed Artificial Intelligence Knowledge Acquisition Artificial Intelligence Applications Natural Language Generation Intelligent Decision Support Systems Natural Language Understanding Cognitive Modeling Hybrid Systems Robotics Genetic Algorithms Vision Evolutionary Programming Planning and Scheduling Knowledge Based Systems Neural Network Knowledge Representation Image Analysis Qualitative Reasoning Automated Reasoning Authors must submit five (5) copies of the completed paper to the AI'94 Conference Secretary, which should be received by or on 15th June 1994. All five (5) copies of the submitted paper must be clearly legible. Neither computer files nor fax submission are acceptable. Papers received after 15th June 1994 will be returned unopened. Notification of receipt will be mailed to the first author (or designated author) soon after receipt. PAPER FORMAT FOR REVIEW All five copies of the submissions must be printed on 8 1/2" x 11" or A4 paper using 12 point type (10 characters per inch for typewriters or 12 point LaTeX article-style). The body of submitted papers must be at most 8 pages, including figures, tables, diagrams, and bibliography, but excluding the title page. Papers exceeding the specified length or not conforming to the formatting requirements are subject to rejection without review. Each copy of the paper must have a title page (separate from the body of the paper) containing the title of the paper, the names and addresses of all authors, telephone number, fax number, electronic mail address, a short (less than 200 word) abstract, topic, and a keyword list. The body of the paper must also contain a copy of the title and abstract without any author details. In addition each page within the paper must be clearly numbered. To facilitate the reviewing process, authors are requested to select their paper's keywords from the list below. Authors are invited to add additional keywords to their keyword list if necessary. Artificial Life, Automated Reasoning, Behaviour-Based Control, Belief Revision, Case-Based Reasoning, Cognitive Modelling, Common Sense Reasoning, Communication and Cooperation, Constraint-Based Reasoning, Computer-Aided Education, Connectionist Models, Corpus-Based Language Analysis, Deduction, Diagnosis, Discourse Analysis, Distributed Problem Solving, Expert Systems, Geometrical Reasoning, Information Extraction, Knowledge Acquisition, Knowledge Representation, Knowledge Sharing Technology, Large Scale Knowledge Engineering, Learning/Adaptation, Machine Learning, Machine Translation, Mathematical Foundations, Multi-Agent Planning, Natural Language Processing, Neural Networks, Nonmonotonic Reasoning, Perception, Planning, Probabilistic Reasoning, Qualitative Reasoning, Reasoning about Action, Reasoning about Physical Systems, Reactivity, Robot Navigation, Robotics, Rule-Based Reasoning, Scheduling, Search, Sensor Interpretation, Sensory Fusion/Fission, Simulation, Situated Cognition, Spatial Reasoning, Speech Recognition, System Architectures, Temporal Reasoning, Terminological Reasoning, Theorem Proving, Truth Maintenance, User Interfaces, Virtual Reality, Vision, 3-D Model Acquisition. Each paper will be carefully reviewed. The criteria that will be given to the conference reviewers have been reproduced below. Authors are advised to bear these criteria in mind while writing their papers: How important is the work reported? Does it attack an important/difficult problem or a peripheral/simple one? Does the approach offered advance the state of the art? Has this or similar work been previously reported? Are the problems and approaches completely new? Is this a novel combination of familiar techniques? Does the paper point out differences from related research? Is it re-inventing the wheel using new terminology? Is the paper technically sound? Does it carefully evaluate the strengths and limitations of its contribution? How are its claims backed up? Is the paper clearly written? Does it motivate the research? Does it describe clearly the algorithms or techniques employed? Does the paper describe previous work? Are the results described and evaluated? Is the paper organised in a logical fashion? PROCEEDINGS PUBLICATION The proceedings of AI'94 will be published by World Scientific Publishers. IMPORTANT DATES Deadline for paper submission : 15th June 1994 Notification of acceptance : 31st July 1994 Camera Ready Copy : 22nd August 1994 Conference : 21st - 25th November 1994 FURTHER INFORMATION All enquires regarding AI'94 and papers submitted to AI'94 should be directed to the following address: AI'94 Conference Secretary Department of Mathematics, Statistics, and Computing Science The University of New England, Armidale, N.S.W., 2351, AUSTRALIA E-mail: ai94@fermat.une.edu.au You may e-mail the following address with the Subject Heading "help" to obtain details on AI'94, UNE, and Armidale. ai94-info@fermat.une.edu.au ai94-info mail server has been established to enable electronic request for information regarding AI'94 Conference. Article 21555 of comp.ai: Xref: glinda.oz.cs.cmu.edu comp.ai:21555 Newsgroups: aus.ai,aus.computers,comp.ai Path: honeydew.srv.cs.cmu.edu!nntp.club.cc.cmu.edu!news.mic.ucla.edu!library.ucla.edu!agate!msuinfo!harbinger.cc.monash.edu.au!yarrina.connect.com.au!sserve!cspyr0.cs.adfa.oz.au!xin From: xin@cspyr0.cs.adfa.oz.au (Xin Yao) Subject: AI'94 Workshop on Evolutionary Computation Message-ID: <1994Apr8.042533.24025@sserve.cc.adfa.oz.au> Keywords: Evolutionary Computation, Optimisation, Learning Sender: news@sserve.cc.adfa.oz.au Organization: Australian Defence Force Academy, Canberra, Australia Date: Fri, 8 Apr 1994 04:25:33 GMT Lines: 83 CALL FOR PAPERS AND PARTICIPATIONS AI'94 WORKSHOP ON EVOLUTIONARY COMPUTATION ========================================== Armidale, NSW, Australia, 22 November 1994 SCOPE ----- AI'94 Workshop on Evolutionary Computation will be held as part of AI'94 (The Seventh Australian Joint Conference on Artificial Intelligence) on 21--25 November 1994 in Armidale, NSW, Australia. People from all areas of evolutionary computation are encouraged to participate in and submit their papers to the workshop. The first such workshop was held on 16 November 1993 in Melbourne, Australia, as the AI'93 Workshop on Evolutionary Computation. Evolutionary computation is the study of computational systems which use ideas and get inspirations from natural evolution and adaptation. Topics of this workshop include, but are not limited to: + Classifier Systems and Other Evolutionary Learning Systems + Evolutionary Artificial Neural Networks + Hybrid Learning Systems + Comparisons Between Different Learning Systems + Evolutionary Optimisation + Self-Organisation + Collective Behaviour + Complexity in Evolutionary Systems + Artificial Life + Evolutionary Approach to Autonomous Robots + Theories of Evolutionary Computation + Parallel Implementations + Applications PAPER SUBMISSION AND PUBLICATION -------------------------------- Authors are invited to submit original papers describing experimental and/or theoretical results from all areas of evolutionary computation. Four hard copies of the *full* paper with no more than 25 11pt single-spaced, single-column pages should be submitted to the following address before *8 August 1994*. Dr X. Yao Department of Computer Science University College, The University of New South Wales Australian Defence Force Academy, Canberra, ACT 2600, Australia Email: xin@csadfa.cs.adfa.oz.au Phone: +61 6 268 8819 Fax: +61 6 268 8581 Authors are strongly encouraged to prepared their manuscripts in LaTex. You may obtain the style file from the publisher's server svserv@vax.ntp.springer.de under directory /tex/latex. The file name is llncs.zip. All accepted papers will be published by Springer-Verlag as a volume in Lecture Notes in Artificial Intelligence. Authors of accepted papers are expected to present their papers at the workshop. Notification to authors will be sent out on *12 September 1994*. The revised final papers should be submitted before *17 October 1994* in order to be included in the proceedings. WORKSHOP ORGANISING COMMITTEE ----------------------------- A/Prof D. Abramson Griffith University Dr E. Lewis University College, UNSW, ADFA Dr B. Marksj\"{o} CSIRO DBCE, Melbourne Dr H.B. Penfold University of Newcastle Dr X. Yao (Chair) University College, UNSW, ADFA IMPORTANT DATES --------------- 8 August 1994 Submission of full papers 12 September 1994 Notification of acceptance/rejection to authors 17 October 1994 Submission of revised final papers Article 22187 of comp.ai: Xref: glinda.oz.cs.cmu.edu comp.ai:22187 comp.ai.genetic:2984 comp.ai.neural-nets:16649 comp.ai.nat-lang:1636 Path: honeydew.srv.cs.cmu.edu!nntp.club.cc.cmu.edu!godot.cc.duq.edu!newsfeed.pitt.edu!gatech!howland.reston.ans.net!EU.net!sunic!trane.uninett.no!nac.no!ifi.uio.no!wabbit.cc.uow.edu.au!news.ci.com.au!metro!grivel!fermat.une.edu.au!ai94 From: ai94@fermat.une.edu.au (Artificial Intelligence Conference 1994) Newsgroups: comp.ai,aus.ai,aus.computers.ai,comp.ai.genetic,comp.ai.neural-nets,comp.ai.nat-lang Subject: 3rd CFP: (AI'94) Seventh Australian Joint Conference on Artificial Intelligence Message-ID: <4781@grivel.une.edu.au> Date: 18 May 94 07:01:23 GMT Sender: usenet@grivel.une.edu.au Followup-To: ai94@fermat.une.edu.au Lines: 408 Nntp-Posting-Host: fermat.une.edu.au T H I R D C A L L F O R P A P E R S Seventh Australian Joint Conference on Artificial Intelligence (AI'94) "Sowing the Seeds for the Future" 21 - 25 November 1994 Proudly sponsored by Microsoft Institute (principal sponsor), IBM, Sun Microsystems, Australian Computer Society, CAMTECH Pty. Ltd., Knowledge Engineering Group - Deakin University, Knowledge Systems Group, Department of Computer Science, University of Sydney, Expert Systems Group - Continuum Australia Limited, Key Centre for Knowledge Based Systems - RMIT, and Department of Mathematics, Statistics, and Computing Science (UNE). Hosted by Department of Mathematics, Statistics, and Computing Science The University of New England,Armidale, N.S.W., 2351, AUSTRALIA AI'94 is the Seventh Australian Joint Conference on Artificial Intelligence. The theme of the conference is "Sowing the Seeds for the Future", which reflects the nature of research in Artificial Intelligence. The goal of the conference is to promote research in artificial intelligence (AI) and scientific interchange among AI researchers and practitioners. AI'94 will be hosted by The Department of Mathematics, Statistics, and Computing Science at The University of New England, between Monday 21st November and Friday 25th November 1994. The conference programme will consist of formal tutorials and workshops on the Monday and Tuesday, a Postgraduate session on Tuesday, and technical paper presentation sessions from Wednesday 23rd to Friday 25th of November. In addition to these sessions there will be three Keynote addresses from renowned international speakers. Wednesday, 23rd November : Professor Wolfgang Wahlster, German Research Center for AI (DFKI) Topic of address : Intellimedia: Planning Language, Graphics and Layout for Adaptive Information Presentation Wolfgang Wahlster is a Professor of Artificial Intelligence in the Department of Computer Science at the University of Saarbruecken, Germany where he currently serves as a Scientific Director of the German Research Center for Artificial Intelligence (DFKI). Since 1975 he has been the principal investigator in various language projects, including HAM-ANS, WISBER, SC, XTRA, VITRA and WIP. He has published over 100 technical papers on natural language processing. His current research includes intelligent multimodal interfaces, user modeling, natural language scene description, intelligent help systems, and deductive plan recognition and generation. Prof. Wahlster is on the editorial boards of various international journals and book series such as Artificial Intelligence, Applied Artificial Intelligence, User Modeling and User-adapted Interaction, Symbolic Computation and the MIT-ACL series. He is a AAAI Fellow and a recipient of the Fritz Winter Award, one of the most prestigious awards for engineering sciences in Germany, for his research on cooperative user interfaces. Prof. Wahlster served as the Conference Chair for IJCAI-93 in Chambery and the Chair of the Board of Trustees of IJCAII from 1991 -1993. Thursday, 24th November : Professor Katia Sycara, Carnegie Mellon University Topic of address : The Present and Future of Distributed Artificial Intelligence Katia Sycara is a Research Scientist in the School of Computer Science at Carnegie Mellon University. She is also Director of the Enterprise Integration Laboratory. She is directing and conducting research aimed at developing decision support systems for integrating organisational decision making. Her doctoral research contributed to the definition of the case-based reasoning paradigm. She has been Principal Investigator of various government and industry funded research (e.g. distributed scheduling, concurrent engineering, enterprise integration, case-based Engineering design, crisis action planning). Dr. Sycara is the author of a book on manufacturing and over 70 technical papers dealing with negotiation, distributed problem solving, case-based reasoning, integration of case-based reasoning with other problem solving methods, and constraint-based reasoning. She is the Area Editor for AI and Management Science for the journal "Group Decision and Negotiation" and on the editorial board of "AI in Engineering" and "Concurrent Engineering: Research and Applications". She is a member of AAAI, ACM, IEEE, and the Institute for Management Science (TIMS). Friday, 25th November : Professor John F. Sowa, State University of New York - Binghamton Topic of Address : Sharing and Integrating Knowledge Bases John F. Sowa is the author of the book Conceptual Structures, which in the past ten years has led to a world-wide movement of people who are using, implementing, and extending the theory of conceptual graphs. He had been working at IBM for 30 years on various aspects of computer systems design and development, especially artificial intelligence and computational linguistics. Now, he is teaching, writing, and working on standards for conceptual schemas with the American National Standards Institute (ANSI) and the International Standards Organization (ISO). PROGRAM COMMITTEE Dr. Chengqi Zhang (co-chair); UNE Dr. Dickson Lukose; UNE Prof. John Debenham (co-chair); UTS Dr. Anand Rao; AAII A/Prof. Mike Brooks; Adelaide A/Prof. Claude Sammut; UNSW Dr. Jennie Clothier; DSTO A/Prof. Liz Sonenberg; Melbourne Dr. Robert Dale; Microsoft Prof. Rodney Topor; Griffith A/Prof. Wee Leng Goh; NTU, Singapore Dr. Wayne Wobcke; Sydney Mr. Andy Horsfall; Fujitsu Dr. Xindong Wu; James Cook Prof. Ray Jarvis; Monash Dr. Xin Yao; ADFA Dr. Chris Leckie; TRL Dr. Waikiang Yeap; Otago, N.Z. Dr. Craig Lindley; CSIRO A/Prof. David W. Russell, USA ORGANISING COMMITTEE Dr. Dickson Lukose (chair) Dr. Chengqi Zhang Mr. Prakash Bhandari Mr. Allan Williams (secretary) Dr. Gregory Zevin Ms. Gabrielle Aldridge We invite authors to submit papers describing both experimental and theoretical results from all stages of AI research. We encourage submission of papers that describe innovative concepts, techniques, perspectives, or observations that are not yet supported by mature results. Such submissions must include substantial analysis of the ideas, the technology needed to realise them, and their potential impact. Papers describing applied AI are particularly solicited. Topics of interest include, but are not limited to: Machine Learning Distributed Artificial Intelligence Knowledge Acquisition Artificial Intelligence Applications Natural Language Generation Intelligent Decision Support Systems Natural Language Understanding Cognitive Modeling Hybrid Systems Robotics Genetic Algorithms Vision Evolutionary Programming Planning and Scheduling Knowledge Based Systems Neural Network Knowledge Representation Image Analysis Qualitative Reasoning Automated Reasoning Authors must submit five (5) copies of the completed paper to the AI'94 Conference Secretary, which should be received by or on 15th June 1994. All five (5) copies of the submitted paper must be clearly legible. Neither computer files nor fax submission are acceptable. Papers received after 15th June 1994 will be returned unopened. Notification of receipt will be mailed to the first author (or designated author) soon after receipt. PAPER FORMAT FOR REVIEW All five copies of the submissions must be printed on 8 1/2" x 11" or A4 paper using 12 point type (10 characters per inch for typewriters or 12 point LaTeX article-style). The body of submitted papers must be at most 8 pages, including figures, tables, diagrams, and bibliography, but excluding the title page. Papers exceeding the specified length or not conforming to the formatting requirements are subject to rejection without review. Each copy of the paper must have a title page (separate from the body of the paper) containing the title of the paper, the names and addresses of all authors, telephone number, fax number, electronic mail address, a short (less than 200 word) abstract, topic, and a keyword list. The body of the paper must also contain a copy of the title and abstract without any author details. In addition each page within the paper must be clearly numbered. To facilitate the reviewing process, authors are requested to select their paper's keywords from the list below. Authors are invited to add additional keywords to their keyword list if necessary. Artificial Life, Automated Reasoning, Behaviour-Based Control, Belief Revision, Case-Based Reasoning, Cognitive Modelling, Common Sense Reasoning, Communication and Cooperation, Constraint-Based Reasoning, Computer-Aided Education, Connectionist Models, Corpus-Based Language Analysis, Deduction, Diagnosis, Discourse Analysis, Distributed Problem Solving, Expert Systems, Geometrical Reasoning, Information Extraction, Knowledge Acquisition, Knowledge Representation, Knowledge Sharing Technology, Large Scale Knowledge Engineering, Learning/Adaptation, Machine Learning, Machine Translation, Mathematical Foundations, Multi-Agent Planning, Natural Language Processing, Neural Networks, Nonmonotonic Reasoning, Perception, Planning, Probabilistic Reasoning, Qualitative Reasoning, Reasoning about Action, Reasoning about Physical Systems, Reactivity, Robot Navigation, Robotics, Rule-Based Reasoning, Scheduling, Search, Sensor Interpretation, Sensory Fusion/Fission, Simulation, Situated Cognition, Spatial Reasoning, Speech Recognition, System Architectures, Temporal Reasoning, Terminological Reasoning, Theorem Proving, Truth Maintenance, User Interfaces, Virtual Reality, Vision, 3-D Model Acquisition. Each paper will be carefully reviewed. The criteria that will be given to the conference reviewers have been reproduced below. Authors are advised to bear these criteria in mind while writing their papers: How important is the work reported? Does it attack an important/difficult problem or a peripheral/simple one? Does the approach offered advance the state of the art? Has this or similar work been previously reported? Are the problems and approaches completely new? Is this a novel combination of familiar techniques? Does the paper point out differences from related research? Is it re-inventing the wheel using new terminology? Is the paper technically sound? Does it carefully evaluate the strengths and limitations of its contribution? How are its claims backed up? Is the paper clearly written? Does it motivate the research? Does it describe clearly the algorithms or techniques employed? Does the paper describe previous work? Are the results described and evaluated? Is the paper organised in a logical fashion? PROCEEDINGS PUBLICATION The proceedings of AI'94 will be published by World Scientific Publishers. IMPORTANT DATES Deadline for paper submission : 15th June 1994 Notification of acceptance : 31st July 1994 Camera Ready Copy : 22nd August 1994 Conference : 21st - 25th November 1994 FURTHER INFORMATION All enquires regarding AI'94 and papers submitted to AI'94 should be directed to the following address: AI'94 Conference Secretary Department of Mathematics, Statistics, and Computing Science The University of New England, Armidale, N.S.W., 2351, AUSTRALIA E-mail: ai94@fermat.une.edu.au You may e-mail the following address with the Subject Heading "help" to obtain details on AI'94, UNE, and Armidale. ai94-info@fermat.une.edu.au ai94-info mail server has been established to enable electronic request for information regarding AI'94 Conference. ------------------------------------------------------------------------------- IMPORTANT DATES FOR AI'94 WORKSHOPS ------------------------------------------------------------------------------- a) 1st Australian Conceptual Structures Workshop Submission Deadline August 31, 1994. Notification of Acceptance September 30, 1994. Camera-ready copy October 15, 1994. b) AI'94 Workshop on Evolutionary Computation Submission Deadline August 8, 1994. Notification of Acceptance September 12, 1994. Camera-ready copy October 17, 1994. c) AI'94 Workshop on Expert Systems in Production use Abstarct Submission Deadline August 31, 1994. Notification of Acceptance September 15, 1994. d) AI'94 Workshop on Knowledge-Based Systems in Natural Resource Management Abstarct Submission Deadline August 31, 1994. Notification of Acceptance September 15, 1994. e) 2nd Australian Workshop on Natural Language Processing Submission of extended abstract August 9, 1994 Notification of acceptance: September 16, 1994 Full paper submission: October 17, 1994 ------------------------------------------------------------------------------- ------------------------------------------------------------------------------- STRUCTURED SEQUENCE TUTORIALS ------------------------------------------------------------------------------- A structured sequence of pre-conference tutorials on several aspects of applied AI has been organised. Tutorial participants will be able to select from a choice of tutorials to suit their specialist requirements. The following are the list of tutorials organised for AI'94. All participants of any of these tutorials may attend the talk entitled "Introduction to Artificial Intelligence". This is a complementary session for all tutorial participants. Guide: (y) - indicate YES (n) - indicate NO (t) - indicate THEORETICAL SESSION (p) - indicate PRACTICAL SESSION (d) - indicate DEMONSTRATION SESSION 1hr - indicate one hour 4s - indicate four sessions 3s - indicate three sessions Note: Each session is One and a Half hours long. -------------------------------------------------------------------------------- No. Tutorial Title Presenters CODE Length Practical -------------------------------------------------------------------------------- [0] Introduction To Artificial Not Confirmed yet AI 1hr - Intelligence [1] Constraint-Based Reasoning Dr. H. W. Guesgen CBR 4s y [2] Fundamentals of Fuzzy Logic Dr. A. Sekercioglu FLFC 3s y and Fuzzy Logic Controllers G.K. Egan [3] An Introduction to Evolutionary Dr. X. Yao EC 3s y Computation [4] Building Intelligent Decision Dr. J. Zeleznikow IDSS 3s y Support Systems through the use Mr. Dan Hunter of multiple reasonig Strategies [5] Intelligent Learning Database Dr Xindong Wu ILDB 3s y Systems [6] Nonmonotonic Reasoning Dr. M-A Williams NMR 3s y Dr. G. Antoniou [7] Theoretical Foundations of Dr. A. G. Hoffman TFML 3s n Machine Learning Dr. Shyam Kapur Dr. Arun Sharma [8] Knowledge Acquisition and Dr. P. Compton KAM 3s d Maintenance with Ripple Down Rules [9] Hybrid (AI symbolic, Dr. Nik K. Kasabov HS 3s d Connectionist, Fuzzy, Chaotic) Systems -------------------------------------------------------------------------------- Tentative Tutorial Timetable -------------------------------------------------------------------------------- Monday 21/11/94 (Day 1) ======================= 9.30 - 10.30: Introduction to AI 10.30 - 11.00: Morning Tea Break 11.00 - 12.30: NMR(t) IDSS(t) EC(t) TFML(t) 12.30 - 2.00: Lunch 2.00 - 3.30: NMR(t) IDSS(t) EC(t) TFML(t) 3.30 - 4.00: Afternoon Tea Break 4.00 - 5.30: NMR(p) IDDS(p) EC(p) TFML(t) Tuesday 22/11/94 (Day 2) ======================== 9.00 - 10.30: CBR(t) ILDB(t) FLFC(t) KAM(t) HS(t) 10.30 - 11.00: Morning Tea Break 11.00 - 12.30: CBR(t) ILDB(t) FLFC(t) KAM(t) HS(t) 12.30 - 2.00: Lunch 2.00 - 3.30: CBR(t) ILDB(p) FLFC(p) KAM(d) HS(d) 3.30 - 4.00: Afternoon Tea Break 4.00 - 5.30: CBR(p) -------------------------------------------------------------------------------- Examples of structured sequence of tutorials: -------------------------------------------------------------------------------- There are couple of structured sequence of tutorials that one could adopt. For example, if the participant is interested in logic/theoretical basis of AI, then he/she may want to select the following sequence: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: NMR(t) 2.00 - 3.30: NMR(t) 4.00 - 5.30: NMR(p) Day 2 9.00 - 10.30: CBR(t) 11.00 - 12.30: CBR(t) 2.00 - 3.30: CBR(t) 4.00 - 5.30: CBR(p) Alternatively, if the participant is more interested in the applications of AI, then the following sequence may be more suitable: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: IDSS(t) 2.00 - 3.30: IDSS(t) 4.00 - 5.30: IDSS(p) Day 2 9.00 - 10.30: FLFC(t) 11.00 - 12.30: FLFC(t) 2.00 - 3.30: FLFC(p) If the interest is in Machine Learning/Knowledge Acquisition, then the possible sequence may be: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: TFML(t) 2.00 - 3.30: TFML(t) 4.00 - 5.30: TFML(t) Day 2 9.00 - 10.30: ILDB(t) or KAM(t) 11.00 - 12.30: ILDB(t) or KAM(t) 2.00 - 3.30: ILDB(p) or KAM(d) Another possible sequence may be: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: EC(t) 2.00 - 3.30: EC(t) 4.00 - 5.30: EC(p) Day 2 9.00 - 10.30: HS(t) 11.00 - 12.30: HS(t) 2.00 - 3.30: HS(d) -------------------------------------------------------------------------------- Further Information: -------------------------------------------------------------------------------- For further information on the structured sequence tutorials, please contact the AI'94 Tutorial Co-ordinator, at the following address: Dr. Dickson Lukose Department of Mathematics, Statistics, and Computing Science University of New England Armidale, N.S.W., 2351 AUSTRALIA e-mail: ai94@fermat.une.edu.au fax. : (+61 67) 73 3312 -------------------------------------------------------------------------------- Article 22188 of comp.ai: Xref: glinda.oz.cs.cmu.edu comp.ai:22188 comp.ai.genetic:2985 comp.ai.neural-nets:16650 comp.ai.nat-lang:1637 Path: honeydew.srv.cs.cmu.edu!nntp.club.cc.cmu.edu!newsfeed.pitt.edu!godot.cc.duq.edu!news.duke.edu!MathWorks.Com!europa.eng.gtefsd.com!howland.reston.ans.net!pipex!sunic!trane.uninett.no!nac.no!ifi.uio.no!wabbit.cc.uow.edu.au!metro!grivel!fermat.une.edu.au!ai94 From: ai94@fermat.une.edu.au (Artificial Intelligence Conference 1994) Newsgroups: comp.ai,aus.ai,aus.computers.ai,comp.ai.genetic,comp.ai.neural-nets,comp.ai.nat-lang Subject: CFPP (AI'94) 1st AUSTRALIAN CONCEPTUAL STRUCTURES WORKSHOP Message-ID: <4782@grivel.une.edu.au> Date: 18 May 94 07:07:34 GMT Sender: usenet@grivel.une.edu.au Followup-To: ai94@fermat.une.edu.au Lines: 121 Nntp-Posting-Host: fermat.une.edu.au C A L L F O R P A P E R S A N D P A R T I C I P A T I O N S 1st AUSTRALIAN CONCEPTUAL STRUCTURES WORKSHOP in association with the Seventh Australian Joint Conference on Artificial Intelligence (AI'94) University of New England, Armidale, N.S.W., Australia, November 22, 1994 THEME Conceptual graphs are a logic-based formalism for knowledge representation based on the existential graphs of Charles S. Peirce and semantic networks. 1994 marks the tenth anniversary of their use. During this time conceptual structures have been widely used as a semantic representation for natural language and as a graphic system of logic for expert systems, theorem provers, and database design. Significant gains have been made in the storage and retrieval of DBMS information coupled with knowledge-based system problem solving capability. Researchers have developed a sizeable software base and continue to build upon it. Successful implementations include: rule-based systems, database systems, knowledge-based systems, knowledge engineering tools, enterprise modelling, management information systems, conceptual information retrieval, medical informatics and natural language applications, among others. Conceptual graphs are being proposed as a basis for the normative language for conceptual schemas by the ANSI X3H4 Committee on Information Resource Dictionary Systems. Conceptual graphs are also proposed with Knowledge Interchange Format (KIF) as the standard for knowledge interchange between computer systems. We encourage the submission of position papers concerning conceptualisation, formation and modelling using conceptual graphs. TOPICS and ISSUES Papers are invited on any aspect of concept analysis, representation, or manipulation involving conceptual graphs. The following topics are of particular interest but others, concerned with conceptual graphs, will be welcome as well. * Theory * Technical developments * Applications * Natural language understanding * Graph notation * Theorem Proving * Ontology INVITED TALK John F. Sowa, SUNY at Binghamton (USA) "Knowledge Representation: Logical, Philosophical, and Computational Foundations" Norman Foo, Sydney Univ. "A Framework for Ontology Revision" PROGRAM COMMITTEE Peter Creasy University of Queensland Peter Eklund Adelaide University (Co-chair) Gerard Ellis University of Queensland (Co-chair) Norman Foo University of Sydney Dickson Lukose University of New England John Sowa SUNY at Binghamton (USA) Eric Tsui Continuum Australia Ltd. IMPORTANT DATES Submission Deadline August 31, 1994. Notification of Acceptance September 30, 1994. Camera-ready copy October 15, 1994. Submissions and enquires to: Gerard Ellis Computer Science Dept, Royal Melbourne Institute of Technology GPO Box 2476V, Melbourne, Victoria, 3001. Ph:61-3-660-2544 FAX:61-3-662-1617 Email: ged@cs.rmit.edu.au ------------------------------------------------------------------------------- IMPORTANT DATES FOR AI'94 WORKSHOPS ------------------------------------------------------------------------------- a) 1st Australian Conceptual Structures Workshop Submission Deadline August 31, 1994. Notification of Acceptance September 30, 1994. Camera-ready copy October 15, 1994. b) AI'94 Workshop on Evolutionary Computation Submission Deadline August 8, 1994. Notification of Acceptance September 12, 1994. Camera-ready copy October 17, 1994. c) AI'94 Workshop on Expert Systems in Production use Abstarct Submission Deadline August 31, 1994. Notification of Acceptance September 15, 1994. d) AI'94 Workshop on Knowledge-Based Systems in Natural Resource Management Abstarct Submission Deadline August 31, 1994. Notification of Acceptance September 15, 1994. e) 2nd Australian Workshop on Natural Language Processing Submission of extended abstract August 9, 1994 Notification of acceptance: September 16, 1994 Full paper submission: October 17, 1994 ------------------------------------------------------------------------------- Article 22189 of comp.ai: Xref: glinda.oz.cs.cmu.edu comp.ai:22189 comp.ai.genetic:2986 comp.ai.neural-nets:16651 comp.ai.nat-lang:1638 Path: honeydew.srv.cs.cmu.edu!nntp.club.cc.cmu.edu!newsfeed.pitt.edu!godot.cc.duq.edu!news.duke.edu!MathWorks.Com!europa.eng.gtefsd.com!howland.reston.ans.net!pipex!sunic!trane.uninett.no!nac.no!ifi.uio.no!wabbit.cc.uow.edu.au!metro!grivel!fermat.une.edu.au!ai94 From: ai94@fermat.une.edu.au (Artificial Intelligence Conference 1994) Newsgroups: comp.ai,aus.ai,aus.computers.ai,comp.ai.genetic,comp.ai.neural-nets,comp.ai.nat-lang Subject: CFPP (AI'94) WORKSHOP ON EVOLUTIONARY COMPUTATION Message-ID: <4783@grivel.une.edu.au> Date: 18 May 94 07:09:21 GMT Sender: usenet@grivel.une.edu.au Followup-To: ai94@fermat.une.edu.au Lines: 109 Nntp-Posting-Host: fermat.une.edu.au C A L L F O R P A P E R S A N D P A R T I C I P A T I O N S AI'94 WORKSHOP ON EVOLUTIONARY COMPUTATION University of New England, Armidale, NSW, Australia, 22 November 1994 SCOPE ----- AI'94 Workshop on Evolutionary Computation will be held as part of AI'94 (The Seventh Australian Joint Conference on Artificial Intelligence) on 21--25 November 1994 in Armidale, NSW, Australia. People from all areas of evolutionary computation are encouraged to participate in and submit their papers to the workshop. The first such workshop was held on 16 November 1993 in Melbourne, Australia, as the AI'93 Workshop on Evolutionary Computation. Evolutionary computation is the study of computational systems which use ideas and get inspirations from natural evolution and adaptation. Topics of this workshop include, but are not limited to: + Classifier Systems and Other Evolutionary Learning Systems + Evolutionary Artificial Neural Networks + Hybrid Learning Systems + Comparisons Between Different Learning Systems + Evolutionary Optimisation + Self-Organisation + Collective Behaviour + Complexity in Evolutionary Systems + Artificial Life + Evolutionary Approach to Autonomous Robots + Theories of Evolutionary Computation + Parallel Implementations + Applications PAPER SUBMISSION AND PUBLICATION -------------------------------- Authors are invited to submit original papers describing experimental and/or theoretical results from all areas of evolutionary computation. Four hard copies of the *full* paper with no more than 25 11pt single-spaced, single-column pages should be submitted to the following address before *8 August 1994*. Dr X. Yao Department of Computer Science University College, The University of New South Wales Australian Defence Force Academy, Canberra, ACT 2600, Australia Email: xin@csadfa.cs.adfa.oz.au Phone: +61 6 268 8819 Fax: +61 6 268 8581 Authors are strongly encouraged to prepared their manuscripts in LaTex. You may obtain the style file from the publisher's server svserv@vax.ntp.springer.de under directory /tex/latex. The file name is llncs.zip. All accepted papers will be published by Springer-Verlag as a volume in Lecture Notes in Artificial Intelligence. Authors of accepted papers are expected to present their papers at the workshop. Notification to authors will be sent out on *12 September 1994*. The revised final papers should be submitted before *17 October 1994* in order to be included in the proceedings. WORKSHOP ORGANISING COMMITTEE ----------------------------- A/Prof D. Abramson Griffith University Dr E. Lewis University College, UNSW, ADFA Dr B. Marksj\"{o} CSIRO DBCE, Melbourne Dr H.B. Penfold University of Newcastle Dr X. Yao (Chair) University College, UNSW, ADFA IMPORTANT DATES --------------- 8 August 1994 Submission of full papers 12 September 1994 Notification of acceptance/rejection to authors 17 October 1994 Submission of revised final papers ------------------------------------------------------------------------------- IMPORTANT DATES FOR AI'94 WORKSHOPS ------------------------------------------------------------------------------- a) 1st Australian Conceptual Structures Workshop Submission Deadline August 31, 1994. Notification of Acceptance September 30, 1994. Camera-ready copy October 15, 1994. b) AI'94 Workshop on Evolutionary Computation Submission Deadline August 8, 1994. Notification of Acceptance September 12, 1994. Camera-ready copy October 17, 1994. c) AI'94 Workshop on Expert Systems in Production use Abstarct Submission Deadline August 31, 1994. Notification of Acceptance September 15, 1994. d) AI'94 Workshop on Knowledge-Based Systems in Natural Resource Management Abstarct Submission Deadline August 31, 1994. Notification of Acceptance September 15, 1994. e) 2nd Australian Workshop on Natural Language Processing Submission of extended abstract August 9, 1994 Notification of acceptance: September 16, 1994 Full paper submission: October 17, 1994 ------------------------------------------------------------------------------- Article 22190 of comp.ai: Xref: glinda.oz.cs.cmu.edu comp.ai:22190 comp.ai.genetic:2987 comp.ai.neural-nets:16652 comp.ai.nat-lang:1639 Path: honeydew.srv.cs.cmu.edu!nntp.club.cc.cmu.edu!newsfeed.pitt.edu!godot.cc.duq.edu!news.duke.edu!MathWorks.Com!europa.eng.gtefsd.com!howland.reston.ans.net!pipex!sunic!trane.uninett.no!nac.no!ifi.uio.no!wabbit.cc.uow.edu.au!metro!grivel!fermat.une.edu.au!ai94 From: ai94@fermat.une.edu.au (Artificial Intelligence Conference 1994) Newsgroups: comp.ai,aus.ai,aus.computers.ai,comp.ai.genetic,comp.ai.neural-nets,comp.ai.nat-lang Subject: CFPP (AI'94) WORKSHOP ON EXPERT SYSTEMS IN PRODUCTION USE Message-ID: <4784@grivel.une.edu.au> Date: 18 May 94 07:11:12 GMT Sender: usenet@grivel.une.edu.au Followup-To: ai94@fermat.une.edu.au Lines: 124 Nntp-Posting-Host: fermat.une.edu.au C A L L F O R P A P E R S A N D P A R T I C I P A T I O N S AI'94 WORKSHOP ON EXPERT SYSTEMS IN PRODUCTION USE University of New England, Armidale, NSW, Australia, 22 November 1994 TOPICS AND ISSUES This workshop focuses on KBS/ES that are already in production use. It is envisaged that the workshop provides an opportunity for academic and industry specialists to discuss the pragmatics of developing, maintaining and gaining organisational acceptance of expert systems. Applications may be running on mainframe, workstations or microcomputers. Although all areas of expert systems applications are welcome, we are particularly interested in applications from the major financial services sectors and defense areas. Typical applications may include: - investment analysis; - loan evaluation; - risk analysis; - stock forecasting; - fraud detection; - claims processing; - underwriting; - help-desk advisory systems; - surveillance systems and - sonar analysis. A range of technical and organisational issues is expected to be covered in the workshop. Issues may include: - choice of hardware and software; - data collection and knowledge acquisition; - efficiency; - cost considerations; - project planning and control; - human computer interface; - user training and acceptance; - management support and - technical support and maintenance. FORMAT AND DURATION OF THE WORKSHOP - Preceded by a series of short presentations (6-8, 20 minutes each) - 2-3 panel sessions on specific issues - Demonstrations - Workshop summary SUBMISSION GUIDELINES Attendance is primarily restricted to participants who actually have a system or systems in production use although there will be places for "attendance only" participants who can demonstrate they have a strong interest in and are active in the field. Applicants who intend to present should send an extended abstract (3-5 pages; around 1000-1500 words). Applicants who seek only to attend should forward a one-page concise summary of their current work. WORKSHOP COMMITTEE Ernest Edmonds Loughborough University of Technology Brian Garner Deakin University Andy Horsfall Fujitsu Australia Daniel O' Leary University of Southern California Anand Rao Australian AI Institute Eric Tsui Continuum Australia IMPORTANT DATES 31st August 1994 Deadline for submission of abstract/summary 15th September 1994 Notification of acceptance/rejection 22nd November 1994 Workshop 21st - 25th November 1994 Conference FURTHER INFORMATION All enquires regarding this workshop and abstract/summary submission should be directed to the following address: Dr. Eric Tsui Expert Systems Group Continuum Australia Ltd 201 Miller St North Sydney NSW 2060 Ph. 02-2281152 Fax 02-2214808 Email: eric@cs.su.oz.au ------------------------------------------------------------------------------- IMPORTANT DATES FOR AI'94 WORKSHOPS ------------------------------------------------------------------------------- a) 1st Australian Conceptual Structures Workshop Submission Deadline August 31, 1994. Notification of Acceptance September 30, 1994. Camera-ready copy October 15, 1994. b) AI'94 Workshop on Evolutionary Computation Submission Deadline August 8, 1994. Notification of Acceptance September 12, 1994. Camera-ready copy October 17, 1994. c) AI'94 Workshop on Expert Systems in Production use Abstarct Submission Deadline August 31, 1994. Notification of Acceptance September 15, 1994. d) AI'94 Workshop on Knowledge-Based Systems in Natural Resource Management Abstarct Submission Deadline August 31, 1994. Notification of Acceptance September 15, 1994. e) 2nd Australian Workshop on Natural Language Processing Submission of extended abstract August 9, 1994 Notification of acceptance: September 16, 1994 Full paper submission: October 17, 1994 ------------------------------------------------------------------------------- Article 22191 of comp.ai: Xref: glinda.oz.cs.cmu.edu comp.ai:22191 comp.ai.genetic:2988 comp.ai.neural-nets:16653 comp.ai.nat-lang:1640 Path: honeydew.srv.cs.cmu.edu!nntp.club.cc.cmu.edu!newsfeed.pitt.edu!godot.cc.duq.edu!news.duke.edu!MathWorks.Com!europa.eng.gtefsd.com!howland.reston.ans.net!pipex!sunic!trane.uninett.no!nac.no!ifi.uio.no!wabbit.cc.uow.edu.au!metro!grivel!fermat.une.edu.au!ai94 From: ai94@fermat.une.edu.au (Artificial Intelligence Conference 1994) Newsgroups: comp.ai,aus.ai,aus.computers.ai,comp.ai.genetic,comp.ai.neural-nets,comp.ai.nat-lang Subject: CFPP (AI'94) WORKSHOP ON KNOWLEDGE-BASED SYSTEMS IN NATURAL RESOURCE MANAGEMENT Message-ID: <4785@grivel.une.edu.au> Date: 18 May 94 07:12:58 GMT Sender: usenet@grivel.une.edu.au Followup-To: ai94@fermat.une.edu.au Lines: 80 Nntp-Posting-Host: fermat.une.edu.au C A L L F O R P A P E R S A N D P A R T I C I P A T I O N S AI'94 WORKSHOP ON KNOWLEDGE-BASED SYSTEMS IN NATURAL RESOURCE MANAGEMENT University of New England, Armidale, N.S.W., Australia, November 22, 1994 THEME Knowledge-based systems have been used with varying degrees of success in natural resource management for some time, in areas like agriculture, fisheries, spatial modelling of landscapes, and forestry. This workshop aims to look at what features successful systems do have, at problem areas, and at fruitful directions for further development. In addition to applications, the focus will be on methods of knowledge acquisition and representation, user and system interface, the integration of different forms of information technology, and the possibilities for knowledge sharing with similar systems. Of particular interest are papers addressing special difficulties which arise in the development of knowledge-based systems in this domain. The editor of the International Journal of Applied Expert Systems has agreed to publish a special issue based on workshop papers. The papers will be subject to another referring, and four will be selected. Please send correspondence, including papers to John Weckert School of Information Studies Charles Sturt University Locked Bag 675 Wagga Wagga NSW 2678 Telephone: 069 332372 Fax: 069 332733 Email: jweckert@csu.edu.au ORGANISING COMMITTEE John Weckert School of Information Studies, Charles Sturt University, NSW Craig McDonald School of Information Studies, Charles Sturt University, NSW Zvi Hochman Agricultural Research Institute, Wollongbar, NSW Bob McKay Department of Computer Science, ADFA, ACT Brian Turner Department of Forestry, Australian National University, ACT IMPORTANT DATES 31st August 1994 Deadline for submission of abstract/summary 15th September 1994 Notification of acceptance/rejection 22nd November 1994 Workshop 21st - 25th November 1994 Conference ------------------------------------------------------------------------------- IMPORTANT DATES FOR AI'94 WORKSHOPS ------------------------------------------------------------------------------- a) 1st Australian Conceptual Structures Workshop Submission Deadline August 31, 1994. Notification of Acceptance September 30, 1994. Camera-ready copy October 15, 1994. b) AI'94 Workshop on Evolutionary Computation Submission Deadline August 8, 1994. Notification of Acceptance September 12, 1994. Camera-ready copy October 17, 1994. c) AI'94 Workshop on Expert Systems in Production use Abstarct Submission Deadline August 31, 1994. Notification of Acceptance September 15, 1994. d) AI'94 Workshop on Knowledge-Based Systems in Natural Resource Management Abstarct Submission Deadline August 31, 1994. Notification of Acceptance September 15, 1994. e) 2nd Australian Workshop on Natural Language Processing Submission of extended abstract August 9, 1994 Notification of acceptance: September 16, 1994 Full paper submission: October 17, 1994 ------------------------------------------------------------------------------- Article 22192 of comp.ai: Xref: glinda.oz.cs.cmu.edu comp.ai:22192 comp.ai.genetic:2989 comp.ai.neural-nets:16654 comp.ai.nat-lang:1641 Path: honeydew.srv.cs.cmu.edu!nntp.club.cc.cmu.edu!newsfeed.pitt.edu!godot.cc.duq.edu!news.duke.edu!MathWorks.Com!europa.eng.gtefsd.com!howland.reston.ans.net!pipex!sunic!trane.uninett.no!nac.no!ifi.uio.no!wabbit.cc.uow.edu.au!metro!grivel!fermat.une.edu.au!ai94 From: ai94@fermat.une.edu.au (Artificial Intelligence Conference 1994) Newsgroups: comp.ai,aus.ai,aus.computers.ai,comp.ai.genetic,comp.ai.neural-nets,comp.ai.nat-lang Subject: CFPP (AI'94) 2nd AUSTRALIAN WORKSHOP ON NATURAL LANGUAGE PROCESSING Message-ID: <4786@grivel.une.edu.au> Date: 18 May 94 07:15:19 GMT Sender: usenet@grivel.une.edu.au Followup-To: ai94@fermat.une.edu.au Lines: 151 Nntp-Posting-Host: fermat.une.edu.au C A L L F O R P A P E R S A N D P A R T I C I P A T I O N S 2nd Australian Workshop on Natural Language Processing in association with the Seventh Australian Joint Conference on Artificial Intelligence (AI'94) University of New England, Armidale, N.S.W., Australia 22 November 1994 (preceding AI'94) AIMS The principal aim is to bring together researchers in Natural Language Processing and related fields. The workshop is intended to be a forum for discussing various facets of problems associated with natural language understanding and generation by machines, and emerging methodologies for addressing these problems. Currently those active in these areas of research in Australia have very few avenues for the discussion and sharing of ideas and points of view. This workshop builds on the success of the Workshop on Natural Language Processing associated with AI'93 to address this problem. Hopefully by bringing together researchers in this field, we can share ideas and learn from one another, thus encouraging further development and consolidation of research in Natural Language Processing in Australia. SCOPE The primary aim of this workshop is to help engender a sense of community amongst Australian and New Zealand-based researchers in NLP; to this end, we welcome submissions on any aspect from the full range of ongoing work in natural language processing, including but not limited to: * computational morphology * parsing and parser construction * syntactic and semantic interpretation * pragmatic and contextual interpretation * natural language processing techniques for spoken interaction * user modelling * text generation/realization for dialogues * language translation systems To help focus the workshop, we intend to allocate a portion of the day to discussions about one specific theme; given the recent high level of public interest in the concept of the information superhighway, we would particularly appreciate submissions which address issues related to NL access to online services. We therefore ask contributors to look for connections of their work to this topic. The remainder of the workshop will be available for broader discussions on a wider range of issues. PARTICIPATION Attendance to this one-day workshop will be limited to about 20 active participants selected by the organisers on the basis of their submissions. Submissions may be either a short description of the authors' current research, specifying why their contribution to the workshop would be of interest, or an extended abstract. Extended abstracts will be reviewed and authors of accepted abstracts will be given the opportunity to expand their ideas into full papers which will appear in the proceedings of the workshop. Depending on the submissions the workshop organisers may schedule discussion sessions based on issues of common interest. SUBMISSION Four hard copies of submissions must be received by August 9, 1993. Electronic submissions in postscript are also acceptable. The submissions should take one of the following forms: - extended abstracts of papers to be presented at the workshop (about 2000 words) - descriptions of current research to justify attendance (about 1000 words), no presentation at the workshop All submissions should be sent to the following address: Ingrid Zukerman Department of Computer Science Monash University Clayton, VICTORIA 3168 AUSTRALIA DEADLINES Submission of extended abstract or research description: August 9, 1994 Notification of acceptance: September 16, 1994 Full paper submission: October 17, 1994 WORKSHOP ORGANISERS Robert Dale Email: rdale@microsoft.com Address: Microsoft Institute for Advanced Software Technology 65 Epping Road North Ryde, NSW 2113 Phone: (+61) 2 870 2370 Bhavani Raskutti Email: b.raskutti@trl.oz.au Address: Telecom Australia Research Laboratories PO Box 249 Clayton, VICTORIA 3168 Phone: (+61) 3 253 6314 Chris Rowles Email: c.rowles@trl.oz.au Address: as above Phone: (+61) 3 253 6244 Ingrid Zukerman Email: ingrid@bruce.cs.monash.edu.au Address: Dept. of Computer Science Monash University Clayton, VICTORIA 3168 AUSTRALIA Phone: (+61) 3 905 5202 ------------------------------------------------------------------------------- IMPORTANT DATES FOR AI'94 WORKSHOPS ------------------------------------------------------------------------------- a) 1st Australian Conceptual Structures Workshop Submission Deadline August 31, 1994. Notification of Acceptance September 30, 1994. Camera-ready copy October 15, 1994. b) AI'94 Workshop on Evolutionary Computation Submission Deadline August 8, 1994. Notification of Acceptance September 12, 1994. Camera-ready copy October 17, 1994. c) AI'94 Workshop on Expert Systems in Production use Abstarct Submission Deadline August 31, 1994. Notification of Acceptance September 15, 1994. d) AI'94 Workshop on Knowledge-Based Systems in Natural Resource Management Abstarct Submission Deadline August 31, 1994. Notification of Acceptance September 15, 1994. e) 2nd Australian Workshop on Natural Language Processing Submission of extended abstract August 9, 1994 Notification of acceptance: September 16, 1994 Full paper submission: October 17, 1994 ------------------------------------------------------------------------------- Article 22193 of comp.ai: Xref: glinda.oz.cs.cmu.edu comp.ai:22193 comp.ai.genetic:2990 comp.ai.neural-nets:16655 comp.ai.nat-lang:1642 Path: honeydew.srv.cs.cmu.edu!nntp.club.cc.cmu.edu!newsfeed.pitt.edu!gatech!howland.reston.ans.net!EU.net!sunic!trane.uninett.no!nac.no!ifi.uio.no!wabbit.cc.uow.edu.au!metro!grivel!fermat.une.edu.au!ai94 From: ai94@fermat.une.edu.au (Artificial Intelligence Conference 1994) Newsgroups: comp.ai,aus.ai,aus.computers.ai,comp.ai.genetic,comp.ai.neural-nets,comp.ai.nat-lang Subject: CFP (AI'94) TUTORIAL ON CONSTRAINT-BASED REASONING Message-ID: <4787@grivel.une.edu.au> Date: 18 May 94 07:17:24 GMT Sender: usenet@grivel.une.edu.au Followup-To: ai94@fermat.une.edu.au Lines: 259 Nntp-Posting-Host: fermat.une.edu.au C A L L F O R P A R T I C I P A T I O N S AI'94 TUTORIAL ON CONSTRAINT-BASED REASONING by Hans Werner Guesgen Computer Science Department University of Auckland Private Bag 92019 Auckland, New Zealand Email: hans@cs.auckland.ac.nz 22 November 1994 ABSTRACT Many topics in AI became fashionable and then disappeared again. Constraint-based reasoning is certainly an exception to this. Started in the early seventies with algorithms like the well-known Waltz algorithm, the interest in constraint-based reasoning has increased during the successive years, involving researchers from different areas and with different backgrounds. Constraint-based reasoning is applied in a variety of applications. To name a few examples: A project group at the University of Hamburg recently developed a constraint-based system for planning the cabin layout of the newest version of the Airbus (which is considered to be one of the most advanced civil aircrafts currently available). Researchers from NASA recently published their work on a constraint-based system for scheduling observations using the Hubble Space Telescope. Ascent Technology, Inc., developed the constraint-based system ARIS (Airport Resource Information System), which is used by Delta Airlines to help allocate airport gates to arriving flights. DETAILED PLAN This tutorial will give an introduction to the main concepts and techniques of constraint-based reasoning. It is divided into a lecture and a practical part. The latter involves some programming using a toolbox of constraint satisfaction algorithms written in C. The lecture is structured as follows: 1. Motivation for the tutorial and a general introduction to constraint-based reasoning. 2. Presentation of a demo domain for constraint-based reasoning: reasoning about the spatial relationships among items in a typical office. 3. Introduction of a general framework for constraint-based reasoning. 4. Overspecified constraint satisfaction problems: a real-world problem. 5. General techniques for constraint-based reasoning: from local consistency algorithms to backtracking with heuristics. 6. Constraint-based reasoning on parallel and massively parallel computers. 7. Constraint satisfaction as an optimization problem. 8. Some applications of constraint-based reasoning. 9. Summary of the main concepts and conclusion. Recommended reading material is: H.W. Guesgen and J. Hertzberg. A Perspective of Constraint-Based Reasoning. Lecture Notes in Artificial Intelligence 597. Springer, Berlin, Germany, 1992. BIO-DATA OF THE PRESENTER The presenter is a lecturer at the Computer Science Department of the University of Auckland, New Zealand, where he is teaching introductory and advance courses on AI. He is co-chairing the Workshop on Constraint Processing to be held in connection with the 1994 European Conference on Artificial Intelligence (ECAI-94) in Amsterdam, The Netherlands. Together with Joachim Hertzberg from the GMD in St. Augustin, Germany, he presented tutorials on constraint-based reasoning at the British AI conference in 1991 (AISB-91) and at the German AI conference in 1991 (GWAI-91). He is the author/coauthor of two books on constraint-based reasoning, and has more than 30 publications in books, journals, and conference proceedings. PREREQUISITES Participants should have basic knowledge of AI and be able to write simple programs in C. ------------------------------------------------------------------------------- Seventh Australian Joint Conference on Artificial Intelligence (AI'94) STRUCTURED SEQUENCE TUTORIALS A structured sequence of pre-conference tutorials on several aspects of applied AI has been organised. Tutorial participants will be able to select from a choice of tutorials to suit their specialist requirements. The following are the list of tutorials organised for AI'94. All participants of any of these tutorials may attend the talk entitled "Introduction to Artificial Intelligence". This is a complementary session for all tutorial participants. Guide: (y) - indicate YES (n) - indicate NO (t) - indicate THEORETICAL SESSION (p) - indicate PRACTICAL SESSION (d) - indicate DEMONSTRATION SESSION 1hr - indicate one hour 4s - indicate four sessions 3s - indicate three sessions Note: Each session is One and a Half hours long. -------------------------------------------------------------------------------- No. Tutorial Title Presenters CODE Length Practical -------------------------------------------------------------------------------- [0] Introduction To Artificial Not Confirmed yet AI 1hr - Intelligence [1] Constraint-Based Reasoning Dr. H. W. Guesgen CBR 4s y [2] Fundamentals of Fuzzy Logic Dr. A. Sekercioglu FLFC 3s y and Fuzzy Logic Controllers G.K. Egan [3] An Introduction to Evolutionary Dr. X. Yao EC 3s y Computation [4] Building Intelligent Decision Dr. J. Zeleznikow IDSS 3s y Support Systems through the use Mr. Dan Hunter of multiple reasonig Strategies [5] Intelligent Learning Database Dr Xindong Wu ILDB 3s y Systems [6] Nonmonotonic Reasoning Dr. M-A Williams NMR 3s y Dr. G. Antoniou [7] Theoretical Foundations of Dr. A. G. Hoffman TFML 3s n Machine Learning Dr. Shyam Kapur Dr. Arun Sharma [8] Knowledge Acquisition and Dr. P. Compton KAM 3s d Maintenance with Ripple Down Rules [9] Hybrid (AI symbolic, Dr. Nik K. Kasabov HS 3s d Connectionist, Fuzzy, Chaotic) Systems -------------------------------------------------------------------------------- Tentative Tutorial Timetable -------------------------------------------------------------------------------- Monday 21/11/94 (Day 1) ======================= 9.30 - 10.30: Introduction to AI 10.30 - 11.00: Morning Tea Break 11.00 - 12.30: NMR(t) IDSS(t) EC(t) TFML(t) 12.30 - 2.00: Lunch 2.00 - 3.30: NMR(t) IDSS(t) EC(t) TFML(t) 3.30 - 4.00: Afternoon Tea Break 4.00 - 5.30: NMR(p) IDDS(p) EC(p) TFML(t) Tuesday 22/11/94 (Day 2) ======================== 9.00 - 10.30: CBR(t) ILDB(t) FLFC(t) KAM(t) HS(t) 10.30 - 11.00: Morning Tea Break 11.00 - 12.30: CBR(t) ILDB(t) FLFC(t) KAM(t) HS(t) 12.30 - 2.00: Lunch 2.00 - 3.30: CBR(t) ILDB(p) FLFC(p) KAM(d) HS(d) 3.30 - 4.00: Afternoon Tea Break 4.00 - 5.30: CBR(p) -------------------------------------------------------------------------------- Examples of structured sequence of tutorials: -------------------------------------------------------------------------------- There are couple of structured sequence of tutorials that one could adopt. For example, if the participant is interested in logic/theoretical basis of AI, then he/she may want to select the following sequence: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: NMR(t) 2.00 - 3.30: NMR(t) 4.00 - 5.30: NMR(p) Day 2 9.00 - 10.30: CBR(t) 11.00 - 12.30: CBR(t) 2.00 - 3.30: CBR(t) 4.00 - 5.30: CBR(p) Alternatively, if the participant is more interested in the applications of AI, then the following sequence may be more suitable: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: IDSS(t) 2.00 - 3.30: IDSS(t) 4.00 - 5.30: IDSS(p) Day 2 9.00 - 10.30: FLFC(t) 11.00 - 12.30: FLFC(t) 2.00 - 3.30: FLFC(p) If the interest is in Machine Learning/Knowledge Acquisition, then the possible sequence may be: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: TFML(t) 2.00 - 3.30: TFML(t) 4.00 - 5.30: TFML(t) Day 2 9.00 - 10.30: ILDB(t) or KAM(t) 11.00 - 12.30: ILDB(t) or KAM(t) 2.00 - 3.30: ILDB(p) or KAM(d) Another possible sequence may be: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: EC(t) 2.00 - 3.30: EC(t) 4.00 - 5.30: EC(p) Day 2 9.00 - 10.30: HS(t) 11.00 - 12.30: HS(t) 2.00 - 3.30: HS(d) -------------------------------------------------------------------------------- Further Information: -------------------------------------------------------------------------------- For further information on the structured sequence tutorials, please contact the AI'94 Tutorial Co-ordinator, at the following address: Dr. Dickson Lukose Department of Mathematics, Statistics, and Computing Science University of New England Armidale, N.S.W., 2351 AUSTRALIA e-mail: ai94@fermat.une.edu.au fax. : (+61 67) 73 3312 -------------------------------------------------------------------------------- Article 22194 of comp.ai: Xref: glinda.oz.cs.cmu.edu comp.ai:22194 comp.ai.genetic:2991 comp.ai.neural-nets:16656 comp.ai.nat-lang:1643 Path: honeydew.srv.cs.cmu.edu!nntp.club.cc.cmu.edu!godot.cc.duq.edu!news.duke.edu!MathWorks.Com!europa.eng.gtefsd.com!howland.reston.ans.net!EU.net!sunic!trane.uninett.no!nac.no!ifi.uio.no!wabbit.cc.uow.edu.au!metro!grivel!fermat.une.edu.au!ai94 From: ai94@fermat.une.edu.au (Artificial Intelligence Conference 1994) Newsgroups: comp.ai,aus.ai,aus.computers.ai,comp.ai.genetic,comp.ai.neural-nets,comp.ai.nat-lang Subject: CFP (AI'94) TUTORIAL: AN INTRODUCTION TO EVOLUTIONARY COMPUTATION Message-ID: <4788@grivel.une.edu.au> Date: 18 May 94 07:20:00 GMT Sender: usenet@grivel.une.edu.au Followup-To: ai94@fermat.une.edu.au Lines: 237 Nntp-Posting-Host: fermat.une.edu.au C A L L F O R P A R T I C I P A T I O N S AI'94 Tutorial: An Introduction to Evolutionary Computation by Dr X. Yao Department of Computer Science University College, The University of New South Wales Australian Defence Force Academy, Canberra, ACT 2600 Email: xin@csadfa.cs.adfa.oz.au Phone: + 61 6 268 8819 Fax: + 61 6 268 8581 21 November 1994 INTRODUCTION Evolutionary computation is the study of computational systems which use ideas and get inspirations from natural evolution. This full-day tutorial will give a practical introduction to genetic algorithms, evolutionary programming and evolution strategies and present a brief overview of the whole field of evolutionary computation. OUTLINE OF THE TUTORIAL This tutorial will concentrate on evolutionary optimisation although evolutionary learning will also be covered. It consists of three sessions. One of them is allocated for practical experiments in which the participants will have an opportunity to go through some examples and get a better idea of how evolutionary optimisation works in practice. The tutorial is mainly designed for newcomers to the field of evolutionary computation. The topics covered by the tutorial include the following: 1. Introduction (a) A Definition of Evolutionary Computation (b) Major Branches of Evolutionary Computation 2. Evolutionary Algorithms in Optimisation (a) Generate-and-Test Strategies (b) Genetic Algorithms i. A Simple Genetic Algorithm ii. Selection iii. Crossover iv. Mutation v. Other Genetic Operators (c) Evolution Strategies and Evolutionary Programming i. A Brief History ii. Adaptive Mutation (d) Hybrid Algorithms 3. Evolutionary Learning (a) Classifier Systems (b) Evolutionary Artificial Neural Networks (c) Artificial Life 4. Summary BIO-DATA OF THE PRESENTER Dr Xin Yao obtained his PhD in 1990 and is now a lecturer at the Department of Computer Science, University College, the University of New South Wales, Australian Defence Force Academy at Canberra. His research interests include evolutionary artificial neural networks, evolutionary optimisation, simulated annealing, evolutionary and hybrid learning systems, artificial life and computational complexity. He has published more than 20 technical papers in international journals and conference proceedings. He co-organised AI'93 Workshop on Evolutionary Computation at Melbourne and was on the Program Committee of ICYCS'93 at Beijing. He is a member of IEEE and IEEE Computer Society. PREREQUISITES 1. Knowledge of one high-level programming language. 2. Rudimentary knowledge of probability. 3. Rudimentary knowledge of UNIX (required only by the practical session). ------------------------------------------------------------------------------- Seventh Australian Joint Conference on Artificial Intelligence (AI'94) STRUCTURED SEQUENCE TUTORIALS A structured sequence of pre-conference tutorials on several aspects of applied AI has been organised. Tutorial participants will be able to select from a choice of tutorials to suit their specialist requirements. The following are the list of tutorials organised for AI'94. All participants of any of these tutorials may attend the talk entitled "Introduction to Artificial Intelligence". This is a complementary session for all tutorial participants. Guide: (y) - indicate YES (n) - indicate NO (t) - indicate THEORETICAL SESSION (p) - indicate PRACTICAL SESSION (d) - indicate DEMONSTRATION SESSION 1hr - indicate one hour 4s - indicate four sessions 3s - indicate three sessions Note: Each session is One and a Half hours long. -------------------------------------------------------------------------------- No. Tutorial Title Presenters CODE Length Practical -------------------------------------------------------------------------------- [0] Introduction To Artificial Not Confirmed yet AI 1hr - Intelligence [1] Constraint-Based Reasoning Dr. H. W. Guesgen CBR 4s y [2] Fundamentals of Fuzzy Logic Dr. A. Sekercioglu FLFC 3s y and Fuzzy Logic Controllers G.K. Egan [3] An Introduction to Evolutionary Dr. X. Yao EC 3s y Computation [4] Building Intelligent Decision Dr. J. Zeleznikow IDSS 3s y Support Systems through the use Mr. Dan Hunter of multiple reasonig Strategies [5] Intelligent Learning Database Dr Xindong Wu ILDB 3s y Systems [6] Nonmonotonic Reasoning Dr. M-A Williams NMR 3s y Dr. G. Antoniou [7] Theoretical Foundations of Dr. A. G. Hoffman TFML 3s n Machine Learning Dr. Shyam Kapur Dr. Arun Sharma [8] Knowledge Acquisition and Dr. P. Compton KAM 3s d Maintenance with Ripple Down Rules [9] Hybrid (AI symbolic, Dr. Nik K. Kasabov HS 3s d Connectionist, Fuzzy, Chaotic) Systems -------------------------------------------------------------------------------- Tentative Tutorial Timetable -------------------------------------------------------------------------------- Monday 21/11/94 (Day 1) ======================= 9.30 - 10.30: Introduction to AI 10.30 - 11.00: Morning Tea Break 11.00 - 12.30: NMR(t) IDSS(t) EC(t) TFML(t) 12.30 - 2.00: Lunch 2.00 - 3.30: NMR(t) IDSS(t) EC(t) TFML(t) 3.30 - 4.00: Afternoon Tea Break 4.00 - 5.30: NMR(p) IDDS(p) EC(p) TFML(t) Tuesday 22/11/94 (Day 2) ======================== 9.00 - 10.30: CBR(t) ILDB(t) FLFC(t) KAM(t) HS(t) 10.30 - 11.00: Morning Tea Break 11.00 - 12.30: CBR(t) ILDB(t) FLFC(t) KAM(t) HS(t) 12.30 - 2.00: Lunch 2.00 - 3.30: CBR(t) ILDB(p) FLFC(p) KAM(d) HS(d) 3.30 - 4.00: Afternoon Tea Break 4.00 - 5.30: CBR(p) -------------------------------------------------------------------------------- Examples of structured sequence of tutorials: -------------------------------------------------------------------------------- There are couple of structured sequence of tutorials that one could adopt. For example, if the participant is interested in logic/theoretical basis of AI, then he/she may want to select the following sequence: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: NMR(t) 2.00 - 3.30: NMR(t) 4.00 - 5.30: NMR(p) Day 2 9.00 - 10.30: CBR(t) 11.00 - 12.30: CBR(t) 2.00 - 3.30: CBR(t) 4.00 - 5.30: CBR(p) Alternatively, if the participant is more interested in the applications of AI, then the following sequence may be more suitable: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: IDSS(t) 2.00 - 3.30: IDSS(t) 4.00 - 5.30: IDSS(p) Day 2 9.00 - 10.30: FLFC(t) 11.00 - 12.30: FLFC(t) 2.00 - 3.30: FLFC(p) If the interest is in Machine Learning/Knowledge Acquisition, then the possible sequence may be: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: TFML(t) 2.00 - 3.30: TFML(t) 4.00 - 5.30: TFML(t) Day 2 9.00 - 10.30: ILDB(t) or KAM(t) 11.00 - 12.30: ILDB(t) or KAM(t) 2.00 - 3.30: ILDB(p) or KAM(d) Another possible sequence may be: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: EC(t) 2.00 - 3.30: EC(t) 4.00 - 5.30: EC(p) Day 2 9.00 - 10.30: HS(t) 11.00 - 12.30: HS(t) 2.00 - 3.30: HS(d) -------------------------------------------------------------------------------- Further Information: -------------------------------------------------------------------------------- For further information on the structured sequence tutorials, please contact the AI'94 Tutorial Co-ordinator, at the following address: Dr. Dickson Lukose Department of Mathematics, Statistics, and Computing Science University of New England Armidale, N.S.W., 2351 AUSTRALIA e-mail: ai94@fermat.une.edu.au fax. : (+61 67) 73 3312 -------------------------------------------------------------------------------- Article 22195 of comp.ai: Xref: glinda.oz.cs.cmu.edu comp.ai:22195 comp.ai.genetic:2992 comp.ai.neural-nets:16657 comp.ai.nat-lang:1644 Path: honeydew.srv.cs.cmu.edu!nntp.club.cc.cmu.edu!newsfeed.pitt.edu!godot.cc.duq.edu!news.duke.edu!MathWorks.Com!europa.eng.gtefsd.com!howland.reston.ans.net!EU.net!sunic!trane.uninett.no!nac.no!ifi.uio.no!wabbit.cc.uow.edu.au!metro!grivel!fermat.une.edu.au!ai94 From: ai94@fermat.une.edu.au (Artificial Intelligence Conference 1994) Newsgroups: comp.ai,aus.ai,aus.computers.ai,comp.ai.genetic,comp.ai.neural-nets,comp.ai.nat-lang Subject: CFP (AI'94) TUTORIAL ON FUNDAMENTALS OF FUZZY LOGIC AND FUZZY LOGIC CONTROLLERS Message-ID: <4789@grivel.une.edu.au> Date: 18 May 94 07:22:12 GMT Sender: usenet@grivel.une.edu.au Followup-To: ai94@fermat.une.edu.au Lines: 251 Nntp-Posting-Host: fermat.une.edu.au C A L L F O R P A R T I C I P A T I O N S AI'94 Tutorial on Fundamentals of Fuzzy Logic and Fuzzy Logic Controllers by A. Sekercioglu and G. K. Egan Laboratory for Concurrent Computing Systems Swinburne University of Technology {yas,gke}@swin.oz.au 22 November 1994 OVERVIEW Fuzzy logic, along with artificial neural networks and genetic algorithms constitutes a part of computational intelligence which, in recent years, has become a focal point of research. It attempts to model and mathematically encapsulate the approximate reasoning processes under the influence of imprecise, incomplete and vague input data. Fuzzy logic has strengths in modelling highly nonlinear, complex sytems which are commonly encountered in product design, manufacturing and control. One application of fuzzy logic, control systems utilizing some of its concepts has become an important engineering tool. This tutorial presents a comprehensive picture of fundamental issues of fuzzy logic in order to study the structure and operation of fuzzy logic controllers. DETAILED PLAN The tutorial will address these issues: * introduction and approaches to system modeling: comparison of explicit, neural and fuzzy modeling methods, * crisp sets and fuzzy sets, basic concepts, * operations on fuzzy sets, t-norms and t-conorms, * fuzzy relations and composition of fuzzy relations, * fuzzy inference, generalized modus ponens, generalized modus tollens, fuzzy implication functions, * a brief look at intelligent control, * fuzzy logic controllers: operation and examples. Towards the end of the tutorial, a laboratory session is organized. In the laboratory session, participants will be able to test their ideas by writing their fuzzy rule bases for a simulated fuzzy logic controller in the UNIX environment. BIO-DATA OF THE PRESENTER Ahmet Sekercioglu is a researcher at the Laboratory for Concurrent Computing Systems, Swinburne University of Technology and lecturer at the School of Electrical Engineering in the same university. He has obtained his B.Sc. and M.Sc. degrees from the Middle East Technical University, Ankara, in 1982 and 1985 respectively. During the M.Sc. studies he has worked as a research assistant at the Biomedical Engineering laboratory of Middle East Technical University. Later, he has held research and development engineering positions at Aselsan and Ericsson. Currently he is working towards his Ph.D. degree in the domain of Adaptive Fuzzy Systems. Professor Gregory K. Egan is director of Laboratory for Concurrent Computing Systems, Swinburne University of Technology and principal lecturer at the School of Electrical Engineering. He has graduated in Communication Engineering from RMIT in 1975. He has been awarded an M.Sc. degree in computational methods in 1976, and, a Ph.D. in parallel computer architectures in 1979 both at the Victoria University of Manchester. From 1980 onwards he developed and led the computer systems engineering groups and developed teaching programs at RMIT and Swinburne University of Technology. PREREQUISITES This tutorial will present both introductory and in-depth material. So, it will be suitable for both to novices, as well as to those with some background who wish to extend their knowledge in the area of fuzzy logic controllers. No prerequisite knowledge of the field is necessary. However, some background in propositional logic and classical set theory would be helpful. ------------------------------------------------------------------------------- Seventh Australian Joint Conference on Artificial Intelligence (AI'94) STRUCTURED SEQUENCE TUTORIALS A structured sequence of pre-conference tutorials on several aspects of applied AI has been organised. Tutorial participants will be able to select from a choice of tutorials to suit their specialist requirements. The following are the list of tutorials organised for AI'94. All participants of any of these tutorials may attend the talk entitled "Introduction to Artificial Intelligence". This is a complementary session for all tutorial participants. Guide: (y) - indicate YES (n) - indicate NO (t) - indicate THEORETICAL SESSION (p) - indicate PRACTICAL SESSION (d) - indicate DEMONSTRATION SESSION 1hr - indicate one hour 4s - indicate four sessions 3s - indicate three sessions Note: Each session is One and a Half hours long. -------------------------------------------------------------------------------- No. Tutorial Title Presenters CODE Length Practical -------------------------------------------------------------------------------- [0] Introduction To Artificial Not Confirmed yet AI 1hr - Intelligence [1] Constraint-Based Reasoning Dr. H. W. Guesgen CBR 4s y [2] Fundamentals of Fuzzy Logic Dr. A. Sekercioglu FLFC 3s y and Fuzzy Logic Controllers G.K. Egan [3] An Introduction to Evolutionary Dr. X. Yao EC 3s y Computation [4] Building Intelligent Decision Dr. J. Zeleznikow IDSS 3s y Support Systems through the use Mr. Dan Hunter of multiple reasonig Strategies [5] Intelligent Learning Database Dr Xindong Wu ILDB 3s y Systems [6] Nonmonotonic Reasoning Dr. M-A Williams NMR 3s y Dr. G. Antoniou [7] Theoretical Foundations of Dr. A. G. Hoffman TFML 3s n Machine Learning Dr. Shyam Kapur Dr. Arun Sharma [8] Knowledge Acquisition and Dr. P. Compton KAM 3s d Maintenance with Ripple Down Rules [9] Hybrid (AI symbolic, Dr. Nik K. Kasabov HS 3s d Connectionist, Fuzzy, Chaotic) Systems -------------------------------------------------------------------------------- Tentative Tutorial Timetable -------------------------------------------------------------------------------- Monday 21/11/94 (Day 1) ======================= 9.30 - 10.30: Introduction to AI 10.30 - 11.00: Morning Tea Break 11.00 - 12.30: NMR(t) IDSS(t) EC(t) TFML(t) 12.30 - 2.00: Lunch 2.00 - 3.30: NMR(t) IDSS(t) EC(t) TFML(t) 3.30 - 4.00: Afternoon Tea Break 4.00 - 5.30: NMR(p) IDDS(p) EC(p) TFML(t) Tuesday 22/11/94 (Day 2) ======================== 9.00 - 10.30: CBR(t) ILDB(t) FLFC(t) KAM(t) HS(t) 10.30 - 11.00: Morning Tea Break 11.00 - 12.30: CBR(t) ILDB(t) FLFC(t) KAM(t) HS(t) 12.30 - 2.00: Lunch 2.00 - 3.30: CBR(t) ILDB(p) FLFC(p) KAM(d) HS(d) 3.30 - 4.00: Afternoon Tea Break 4.00 - 5.30: CBR(p) -------------------------------------------------------------------------------- Examples of structured sequence of tutorials: -------------------------------------------------------------------------------- There are couple of structured sequence of tutorials that one could adopt. For example, if the participant is interested in logic/theoretical basis of AI, then he/she may want to select the following sequence: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: NMR(t) 2.00 - 3.30: NMR(t) 4.00 - 5.30: NMR(p) Day 2 9.00 - 10.30: CBR(t) 11.00 - 12.30: CBR(t) 2.00 - 3.30: CBR(t) 4.00 - 5.30: CBR(p) Alternatively, if the participant is more interested in the applications of AI, then the following sequence may be more suitable: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: IDSS(t) 2.00 - 3.30: IDSS(t) 4.00 - 5.30: IDSS(p) Day 2 9.00 - 10.30: FLFC(t) 11.00 - 12.30: FLFC(t) 2.00 - 3.30: FLFC(p) If the interest is in Machine Learning/Knowledge Acquisition, then the possible sequence may be: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: TFML(t) 2.00 - 3.30: TFML(t) 4.00 - 5.30: TFML(t) Day 2 9.00 - 10.30: ILDB(t) or KAM(t) 11.00 - 12.30: ILDB(t) or KAM(t) 2.00 - 3.30: ILDB(p) or KAM(d) Another possible sequence may be: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: EC(t) 2.00 - 3.30: EC(t) 4.00 - 5.30: EC(p) Day 2 9.00 - 10.30: HS(t) 11.00 - 12.30: HS(t) 2.00 - 3.30: HS(d) -------------------------------------------------------------------------------- Further Information: -------------------------------------------------------------------------------- For further information on the structured sequence tutorials, please contact the AI'94 Tutorial Co-ordinator, at the following address: Dr. Dickson Lukose Department of Mathematics, Statistics, and Computing Science University of New England Armidale, N.S.W., 2351 AUSTRALIA e-mail: ai94@fermat.une.edu.au fax. : (+61 67) 73 3312 -------------------------------------------------------------------------------- Article 22196 of comp.ai: Xref: glinda.oz.cs.cmu.edu comp.ai:22196 comp.ai.genetic:2993 comp.ai.neural-nets:16658 comp.ai.nat-lang:1645 Path: honeydew.srv.cs.cmu.edu!nntp.club.cc.cmu.edu!newsfeed.pitt.edu!godot.cc.duq.edu!news.duke.edu!eff!usenet.ins.cwru.edu!howland.reston.ans.net!EU.net!sunic!trane.uninett.no!nac.no!ifi.uio.no!wabbit.cc.uow.edu.au!metro!grivel!fermat.une.edu.au!ai94 From: ai94@fermat.une.edu.au (Artificial Intelligence Conference 1994) Newsgroups: comp.ai,aus.ai,aus.computers.ai,comp.ai.genetic,comp.ai.neural-nets,comp.ai.nat-lang Subject: CFP (AI'94) TUTORIAL ON HYBRID (AI SYMBOLIC, CONNECTIONIST, FUZZY, CHAOTIC) SYSTEMS Message-ID: <4790@grivel.une.edu.au> Date: 18 May 94 07:24:24 GMT Sender: usenet@grivel.une.edu.au Followup-To: ai94@fermat.une.edu.au Lines: 270 Nntp-Posting-Host: fermat.une.edu.au C A L L F O R P A R T I C I P A T I O N S AI'94 Tutorial on Hybrid (AI symbolic, Connectionist, Fuzzy, Chaotic) Systems by Dr Nik K Kasabov, Department of Information Science, University of Otago P.O.Box 56, Dunedin, New Zealand Fax: + 64 3 479 8311, Phone: + 64 3 479 8319 email: nkasabov@otago.ac.nz with Dr. LC Jain, Knowledge Engineering Systems Group, University of South Australia The Levels,SA 5095, Australia Phone: (08) 302 3315, Fax: (08) 302 3384 email: etlcj@levels.unisa.edu.au ABSTRACT The current development of knowledge engineering brings to use very powerful methods for representing and dealing with raw data, incomplete or imprecise data and knowledge, uncertainty, common sense knowledge, complex systems. And these are the connectionist methods, the methods of fuzzy logic and the chaos theory, which complement the existing AI symbolic methods. What are the main principles of the symbolic AI methods, the connectionist methods, the methods of fuzzy systems and chaos theory and how can one use if necessary, all of them when creating an information system? This is the main theme of the tutorial. First, the main principles of using symbolic, fuzzy and neuro systems for problem solving will be discussed and compared. Then hybrid systems will be introduced. A hybrid symbolic-fuzzy-neural environment will be used for demonstration and practical examples will be given as illustrations. Different techniques for solving difficult problems in a hybrid environment will be demonstrated. Connectionist and fuzzy systems can compliment each other very well. How to use neural networks for learning fuzzy rules and how to implement fuzzy rules in a connectionist structure will also be discussed. A picture on the current development of fuzzy neurons and fuzzy neural networks will be given. Another topic presented in the tutorial is chaos theory. Chaos theory has tremendous potential in many areas. It is not only about the infinite variety of `patterns' which can be generated by using a chaotic function with a lot of practical applications, but it is also the way we can simulate or approximate complex real systems. Chaotic neurons and chaotic neural networks will be presented with some of their applications. Having different techniques implemented in one hybrid environment facilitates creating more powerful information processing systems than any of its components. So, the main advantage of using hybrid systems is that they have all the advantages of their subsystems which make them very powerful tools especially for solving AI problems. OUTLINE OF THE TUTORIAL 1. An Introduction to the principles of symbolic AI systems, connectionist systems and fuzzy systems 1.1. The symbolic AI methods and the BIG picture of knowledge engineering. 1.2. Neural networks for problem solving: principles; design; applications; practical examples. 1.3. Fuzzy rule-based systems: principles; design; applications; practical examples. 2. Hybrid symbolic-neuro-fuzzy systems 2.1. How to combine symbolic AI, connectionist and fuzzy systems? 2.2. Hybrid system tools. Fuzzy COPE - a hybrid tool based on CLIPS, neural networks and fuzzy systems. 2.3. Fuzzy rules extraction from neural networks. 2.4. Applications of hybrid systems. Practical examples. 3. Fuzzy neurons and fuzzy neural networks 3.1. Fuzzy-, and neo fuzzy neurons 3.2. Fuzzy neural networks and their applications 4. Chaotic systems 4.1. What is chaos? 4.2. Chaotic neurons and chaotic neural networks 5. Further development of the hybrid systems DURATION: 3 academic hours PREREQUISITE KNOWLEDGE: The basics of AI and the principles of expert systems. BIO-DATA OF THE PRESENTERS Dr. Nikola Kasabov is a Senior Lecturer in the Department of Information Science with the University of Otago. Previously he has been an Associate Professor at the Technical University in Sofia and a visiting lecturer at the University of Essex, UK. He has published over 85 papers, 5 books and 5 patents in the area of connectionist and hybrid connectionist systems, fuzzy systems, parallel programming. He is member of IEEE, INNS, NZCS, ENNS, BNNS, PARS and other international organisations. He is currently the Chairman of the ANNES (Artificial Neural Networks and Expert Systems) Special Interest Group in New Zealand which is part of the New Zealand Computer Society; and also - Chairman of the First ANNES'93 international conference; liaison chairman of the ANZIIS'94; member of the program and advisory board of ICONIP'94 and other international conferences on intelligent information systems. Dr L.C. Jain is a Leader of the Knowledge-based Engineering Systems Group(KES), one of the four groups located in the School of Electronic Engineering, University of South Australia. In addition to books, he has published a hosts of invited papers and presented workshops. His interests focus on System design, diagnosis and signal processing using expert systems, neural networks, fuzzy logic, genetic algorithms and chaos theory. ------------------------------------------------------------------------------- Seventh Australian Joint Conference on Artificial Intelligence (AI'94) STRUCTURED SEQUENCE TUTORIALS A structured sequence of pre-conference tutorials on several aspects of applied AI has been organised. Tutorial participants will be able to select from a choice of tutorials to suit their specialist requirements. The following are the list of tutorials organised for AI'94. All participants of any of these tutorials may attend the talk entitled "Introduction to Artificial Intelligence". This is a complementary session for all tutorial participants. Guide: (y) - indicate YES (n) - indicate NO (t) - indicate THEORETICAL SESSION (p) - indicate PRACTICAL SESSION (d) - indicate DEMONSTRATION SESSION 1hr - indicate one hour 4s - indicate four sessions 3s - indicate three sessions Note: Each session is One and a Half hours long. -------------------------------------------------------------------------------- No. Tutorial Title Presenters CODE Length Practical -------------------------------------------------------------------------------- [0] Introduction To Artificial Not Confirmed yet AI 1hr - Intelligence [1] Constraint-Based Reasoning Dr. H. W. Guesgen CBR 4s y [2] Fundamentals of Fuzzy Logic Dr. A. Sekercioglu FLFC 3s y and Fuzzy Logic Controllers G.K. Egan [3] An Introduction to Evolutionary Dr. X. Yao EC 3s y Computation [4] Building Intelligent Decision Dr. J. Zeleznikow IDSS 3s y Support Systems through the use Mr. Dan Hunter of multiple reasonig Strategies [5] Intelligent Learning Database Dr Xindong Wu ILDB 3s y Systems [6] Nonmonotonic Reasoning Dr. M-A Williams NMR 3s y Dr. G. Antoniou [7] Theoretical Foundations of Dr. A. G. Hoffman TFML 3s n Machine Learning Dr. Shyam Kapur Dr. Arun Sharma [8] Knowledge Acquisition and Dr. P. Compton KAM 3s d Maintenance with Ripple Down Rules [9] Hybrid (AI symbolic, Dr. Nik K. Kasabov HS 3s d Connectionist, Fuzzy, Chaotic) Systems -------------------------------------------------------------------------------- Tentative Tutorial Timetable -------------------------------------------------------------------------------- Monday 21/11/94 (Day 1) ======================= 9.30 - 10.30: Introduction to AI 10.30 - 11.00: Morning Tea Break 11.00 - 12.30: NMR(t) IDSS(t) EC(t) TFML(t) 12.30 - 2.00: Lunch 2.00 - 3.30: NMR(t) IDSS(t) EC(t) TFML(t) 3.30 - 4.00: Afternoon Tea Break 4.00 - 5.30: NMR(p) IDDS(p) EC(p) TFML(t) Tuesday 22/11/94 (Day 2) ======================== 9.00 - 10.30: CBR(t) ILDB(t) FLFC(t) KAM(t) HS(t) 10.30 - 11.00: Morning Tea Break 11.00 - 12.30: CBR(t) ILDB(t) FLFC(t) KAM(t) HS(t) 12.30 - 2.00: Lunch 2.00 - 3.30: CBR(t) ILDB(p) FLFC(p) KAM(d) HS(d) 3.30 - 4.00: Afternoon Tea Break 4.00 - 5.30: CBR(p) -------------------------------------------------------------------------------- Examples of structured sequence of tutorials: -------------------------------------------------------------------------------- There are couple of structured sequence of tutorials that one could adopt. For example, if the participant is interested in logic/theoretical basis of AI, then he/she may want to select the following sequence: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: NMR(t) 2.00 - 3.30: NMR(t) 4.00 - 5.30: NMR(p) Day 2 9.00 - 10.30: CBR(t) 11.00 - 12.30: CBR(t) 2.00 - 3.30: CBR(t) 4.00 - 5.30: CBR(p) Alternatively, if the participant is more interested in the applications of AI, then the following sequence may be more suitable: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: IDSS(t) 2.00 - 3.30: IDSS(t) 4.00 - 5.30: IDSS(p) Day 2 9.00 - 10.30: FLFC(t) 11.00 - 12.30: FLFC(t) 2.00 - 3.30: FLFC(p) If the interest is in Machine Learning/Knowledge Acquisition, then the possible sequence may be: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: TFML(t) 2.00 - 3.30: TFML(t) 4.00 - 5.30: TFML(t) Day 2 9.00 - 10.30: ILDB(t) or KAM(t) 11.00 - 12.30: ILDB(t) or KAM(t) 2.00 - 3.30: ILDB(p) or KAM(d) Another possible sequence may be: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: EC(t) 2.00 - 3.30: EC(t) 4.00 - 5.30: EC(p) Day 2 9.00 - 10.30: HS(t) 11.00 - 12.30: HS(t) 2.00 - 3.30: HS(d) -------------------------------------------------------------------------------- Further Information: -------------------------------------------------------------------------------- For further information on the structured sequence tutorials, please contact the AI'94 Tutorial Co-ordinator, at the following address: Dr. Dickson Lukose Department of Mathematics, Statistics, and Computing Science University of New England Armidale, N.S.W., 2351 AUSTRALIA e-mail: ai94@fermat.une.edu.au fax. : (+61 67) 73 3312 -------------------------------------------------------------------------------- Article 22197 of comp.ai: Xref: glinda.oz.cs.cmu.edu comp.ai:22197 comp.ai.genetic:2994 comp.ai.neural-nets:16659 comp.ai.nat-lang:1646 Path: honeydew.srv.cs.cmu.edu!nntp.club.cc.cmu.edu!newsfeed.pitt.edu!godot.cc.duq.edu!news.duke.edu!eff!usenet.ins.cwru.edu!howland.reston.ans.net!pipex!sunic!trane.uninett.no!nac.no!ifi.uio.no!wabbit.cc.uow.edu.au!news.ci.com.au!metro!grivel!fermat.une.edu.au!ai94 From: ai94@fermat.une.edu.au (Artificial Intelligence Conference 1994) Newsgroups: comp.ai,aus.ai,aus.computers.ai,comp.ai.genetic,comp.ai.neural-nets,comp.ai.nat-lang Subject: CFP (AI'94) TUTORIAL ON BUILDING INTELLIGENT DECISION SUPPORT SYSTEMS THROUGH THE USE OF MULTIPLE REASONING STRATEGIES Message-ID: <4791@grivel.une.edu.au> Date: 18 May 94 07:29:40 GMT Sender: usenet@grivel.une.edu.au Followup-To: ai94@fermat.une.edu.au Lines: 280 Nntp-Posting-Host: fermat.une.edu.au C A L L F O R P A R T I C I P A T I O N S AI'94 Tutorial on Building Intelligent Decision Support Systems through the use of multiple reasoning strategies by Dr John Zeleznikow and Mr. Dan Hunter Database Research Laboratory Law School Applied Computing Research Institute University of Melbourne La Trobe University Parkville, Victoria Australia 3083 Australia 3052 johnz@latcs1.lat.oz.au dah@rumpole.law.unimelb.edu.au 21 November 1994 ABSTRACT This tutorial will demonstrate how attendees can move beyond using rule based reasoning to build intelligent decision support systems. In this tutorial we focus upon supplementing deductive reasoning with case based reasoning, neural networks and intelligent information retrieval. Applications of these principles in law, management and finance will be provided. The tutorial will provide background knowledge necessary to understand the lectures of Professor Sowa and Sycara. OUTLINE OF THE TUTORIAL * Introduction - The need for intelligent decision support systems in finance, management and law * Strategies for providing Intelligent Decision Support Systems - Intelligent Information Retrieval, Deductive Reasoning, Case Based Reasoning * Intelligent Information Retrieval - Keyword retrieval, Boolean retrieval, Vector space models, Probabilistic models. * Introduction to deductive reasoning systems - Production rule systems, Logic programming approaches, Object oriented approaches. * Case based reasoning systems - What are based reasoners? Why use case based reasoners? Constructing differing types of case based reasoners. Examples of case based reasoners: MEDIATOR, PERSUADER, CASEY, HYPO, PROTOS Automated design of case bases. * Integrating deductive and case based reasoning - Using the object oriented approach to build integrated systems. The relative benefits and disadvantages of blackboard systems and distributed artificial intelligence to build integrated systems. Examples of integrated systems: ANAPRON, CABARET, GREBE, IKBALS, PROLEXS * Combining information retrieval, deductive reasoning, neural networks and case based reasoning - Conceptual information retrieval and neural networks, Bayesian Inference Networks, hypertext, The DATALEX workstation, AIR and SCALIR, WIN (Westlaw Is Natural), SPLIT UP. * Conclusion ASSOCIATED PRACTICAL WORK There will be two practical components in the tutorial Participants will have the opportunity to examine and use integrated systems built by the tutorial presenters: in particular IKBALS and FLORENCE -- integrated rule based/case based reasoners -- and SPLIT-UP an integrated rule based/neural network system. Participants will build legal knowledge based systems under the guidance of the tutorial presenters. They will have the opportunity to examine how to supplement rule based reasoning with one or more of the reasoning and retrieval approaches discussed in the tutorial. BIO-DATA OF THE TUTORIAL PRESENTERS Dr John Zeleznikow B.Sc,(Hons), PhD., GradDipComp is the Head of the Database Research Laboratory, Applied Computing Research Institute, and a Senior Lecturer in the Department of Computer Science and Computer Engineering, La Trobe University, Melbourne. His area of research involves using multiple reasoning strategies to build intelligent decision support systems. Dr Zeleznikow is the chairman of the Database Technical Committee of the Australian Computer Society and was an associate editor of the Australian Computer Journal. He was the General Chairman of DS-5 an IFIP Conference on the Semantics of Interoperable Databases, and was tutorial chairman of the International Conference on Software Engineering and the Very Large Databases Conference. He will be General Chairman of the Sixth International Conference on Artificial Intelligence and Law (an ACM conference). He has written over 50 refereed papers and edited five books. Daniel Hunter BSc LL.B (Hons) is a lecturer at the School of Law, University of Melbourne. He is joint editor of Computers and Law, the publication of the Austrlaian and New Zealand Societies for Computers and Law. He is a Barrister and Solicitor of the Supreme Court of Victoria and High Court of Australia, and has worked as a computer programmer and in practice as a solicitor. Together, Dr Zeleznikow and Mr Hunter have written a number of articles on artificial intelligence and law. They have recently published a book, in the Kluwer Law and Taxation Series, `Building Intelligent Legal Information Systems - Representation and Reasoning in Law'. Together they are to edit a special edition of the journal Applied Expert Systems devoted to the topic Legal Expert Systems, and gave a tutorial at the Fourth National Conference on Law, Computers and Artificial Intelligence (Exeter, United Kingdom). PREREQUISITES A basic knowledge of deductive reasoning and rule based expert systems. ------------------------------------------------------------------------------- Seventh Australian Joint Conference on Artificial Intelligence (AI'94) STRUCTURED SEQUENCE TUTORIALS A structured sequence of pre-conference tutorials on several aspects of applied AI has been organised. Tutorial participants will be able to select from a choice of tutorials to suit their specialist requirements. The following are the list of tutorials organised for AI'94. All participants of any of these tutorials may attend the talk entitled "Introduction to Artificial Intelligence". This is a complementary session for all tutorial participants. Guide: (y) - indicate YES (n) - indicate NO (t) - indicate THEORETICAL SESSION (p) - indicate PRACTICAL SESSION (d) - indicate DEMONSTRATION SESSION 1hr - indicate one hour 4s - indicate four sessions 3s - indicate three sessions Note: Each session is One and a Half hours long. -------------------------------------------------------------------------------- No. Tutorial Title Presenters CODE Length Practical -------------------------------------------------------------------------------- [0] Introduction To Artificial Not Confirmed yet AI 1hr - Intelligence [1] Constraint-Based Reasoning Dr. H. W. Guesgen CBR 4s y [2] Fundamentals of Fuzzy Logic Dr. A. Sekercioglu FLFC 3s y and Fuzzy Logic Controllers G.K. Egan [3] An Introduction to Evolutionary Dr. X. Yao EC 3s y Computation [4] Building Intelligent Decision Dr. J. Zeleznikow IDSS 3s y Support Systems through the use Mr. Dan Hunter of multiple reasonig Strategies [5] Intelligent Learning Database Dr Xindong Wu ILDB 3s y Systems [6] Nonmonotonic Reasoning Dr. M-A Williams NMR 3s y Dr. G. Antoniou [7] Theoretical Foundations of Dr. A. G. Hoffman TFML 3s n Machine Learning Dr. Shyam Kapur Dr. Arun Sharma [8] Knowledge Acquisition and Dr. P. Compton KAM 3s d Maintenance with Ripple Down Rules [9] Hybrid (AI symbolic, Dr. Nik K. Kasabov HS 3s d Connectionist, Fuzzy, Chaotic) Systems -------------------------------------------------------------------------------- Tentative Tutorial Timetable -------------------------------------------------------------------------------- Monday 21/11/94 (Day 1) ======================= 9.30 - 10.30: Introduction to AI 10.30 - 11.00: Morning Tea Break 11.00 - 12.30: NMR(t) IDSS(t) EC(t) TFML(t) 12.30 - 2.00: Lunch 2.00 - 3.30: NMR(t) IDSS(t) EC(t) TFML(t) 3.30 - 4.00: Afternoon Tea Break 4.00 - 5.30: NMR(p) IDDS(p) EC(p) TFML(t) Tuesday 22/11/94 (Day 2) ======================== 9.00 - 10.30: CBR(t) ILDB(t) FLFC(t) KAM(t) HS(t) 10.30 - 11.00: Morning Tea Break 11.00 - 12.30: CBR(t) ILDB(t) FLFC(t) KAM(t) HS(t) 12.30 - 2.00: Lunch 2.00 - 3.30: CBR(t) ILDB(p) FLFC(p) KAM(d) HS(d) 3.30 - 4.00: Afternoon Tea Break 4.00 - 5.30: CBR(p) -------------------------------------------------------------------------------- Examples of structured sequence of tutorials: -------------------------------------------------------------------------------- There are couple of structured sequence of tutorials that one could adopt. For example, if the participant is interested in logic/theoretical basis of AI, then he/she may want to select the following sequence: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: NMR(t) 2.00 - 3.30: NMR(t) 4.00 - 5.30: NMR(p) Day 2 9.00 - 10.30: CBR(t) 11.00 - 12.30: CBR(t) 2.00 - 3.30: CBR(t) 4.00 - 5.30: CBR(p) Alternatively, if the participant is more interested in the applications of AI, then the following sequence may be more suitable: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: IDSS(t) 2.00 - 3.30: IDSS(t) 4.00 - 5.30: IDSS(p) Day 2 9.00 - 10.30: FLFC(t) 11.00 - 12.30: FLFC(t) 2.00 - 3.30: FLFC(p) If the interest is in Machine Learning/Knowledge Acquisition, then the possible sequence may be: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: TFML(t) 2.00 - 3.30: TFML(t) 4.00 - 5.30: TFML(t) Day 2 9.00 - 10.30: ILDB(t) or KAM(t) 11.00 - 12.30: ILDB(t) or KAM(t) 2.00 - 3.30: ILDB(p) or KAM(d) Another possible sequence may be: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: EC(t) 2.00 - 3.30: EC(t) 4.00 - 5.30: EC(p) Day 2 9.00 - 10.30: HS(t) 11.00 - 12.30: HS(t) 2.00 - 3.30: HS(d) -------------------------------------------------------------------------------- Further Information: -------------------------------------------------------------------------------- For further information on the structured sequence tutorials, please contact the AI'94 Tutorial Co-ordinator, at the following address: Dr. Dickson Lukose Department of Mathematics, Statistics, and Computing Science University of New England Armidale, N.S.W., 2351 AUSTRALIA e-mail: ai94@fermat.une.edu.au fax. : (+61 67) 73 3312 -------------------------------------------------------------------------------- Article 22198 of comp.ai: Xref: glinda.oz.cs.cmu.edu comp.ai:22198 comp.ai.genetic:2995 comp.ai.neural-nets:16660 comp.ai.nat-lang:1647 Path: honeydew.srv.cs.cmu.edu!nntp.club.cc.cmu.edu!newsfeed.pitt.edu!godot.cc.duq.edu!news.duke.edu!eff!usenet.ins.cwru.edu!howland.reston.ans.net!EU.net!sunic!trane.uninett.no!nac.no!ifi.uio.no!wabbit.cc.uow.edu.au!news.ci.com.au!metro!grivel!fermat.une.edu.au!ai94 From: ai94@fermat.une.edu.au (Artificial Intelligence Conference 1994) Newsgroups: comp.ai,aus.ai,aus.computers.ai,comp.ai.genetic,comp.ai.neural-nets,comp.ai.nat-lang Subject: CFP (AI'94) TUTORIAL ON INTELLIGENT LEARNING DATABASE SYSTEMS Message-ID: <4792@grivel.une.edu.au> Date: 18 May 94 07:31:17 GMT Sender: usenet@grivel.une.edu.au Followup-To: ai94@fermat.une.edu.au Lines: 255 Nntp-Posting-Host: fermat.une.edu.au Seventh Australian Joint Conference on Artificial Intelligence (AI'94) "Sowing the Seeds for the Future" C A L L F O R P A R T I C I P A T I O N S AI'94 Tutorial on Intelligent Learning Database Systems by Dr Xindong Wu Dept. of Computer Science, James Cook University, Townsville, Queensland 4811, Australia xindong@coral.cs.jcu.edu.au 22 November 1994 ABSTRACT Knowledge acquisition from databases is a research frontier for both database technology and machine learning (ML) techniques, and has seen sustained research over recent years. It also acts as a link between the two fields, thus offering a dual benefit. Firstly, since database technology has already found wide application in many fields, ML research obviously stands to gain from this greater exposure and established technological foundation. Secondly, ML techniques can augment the ability of existing database systems to represent, acquire, and process a collection of expertise such as those which form part of the semantics of many advanced applications (e.g. CAD/CAM). This full-day tutorial will present and discuss techniques for the following 3 interconnected phases in constructing intelligent learning database systems: (1) Translation of standard database information into a form suitable for use by a rule-based system; (2) Using machine learning techniques to produce rule bases from databases; and (3) Interpreting the rules produced to solve users' problems and/or reduce data spaces. It will suit a wide audience (including postgraduate students and industrial people) from databases, expert systems, and machine learning. CONTENTS 1. Knowledge Acquisition from Databases: Problem and Domain 1.1 Problems in Conventional Databases 1.2 Research Topics in Intelligent Databases 1.3 Requirements for Knowledge Discovery in Databases 2. Typical Inductive Learning Algorithms 2.1 The ID3 Family 2.2 The AQ Family 2.3 The HCV Family 3. Integrating More Semantic Information into Data Models 3.1 The E-R Model 3.2 Deductive and Object-Oriented Databases 3.3 More Expressive Representations 4. An Intelligent Learning Database System To introduce a PC shell developed at Edinburgh 5. Conclusions and Research Directions in the Field 6. A Practical Component Using a PC Lab COURSE MATERIAL - Xindong Wu, Research Issues in Intelligent Learning Database Systems, Proceedings of the Seventh Annual Florida AI Research Symposium, Pensacola Beach, Florida, U.S.A., May 5-7, 1994, 137--141. - Xindong Wu, Inductive Learning: Algorithms and Frontiers, Artificial Intelligence Review, 7(1993), 2: 93-108. - Xindong Wu, KEshell2: An Intelligent Learning Data Base System, Research and Development in Expert Systems IX, M.A. Bramer and R.W. Milne (Eds.), Cambridge University Press, U.K., 1992, 253--272. BIO-DATA OF THE PRESENTER Dr Xindong Wu received his first and Master's degrees in Computer Science from Hefei University of Technology, China, and his Ph.D. in Artificial Intelligence from the University of Edinburgh, Britain. In the past, he has authored 2 technical books, Expert Systems Technology (1988) and Constructing Expert Systems (1990). He has also published over 60 papers in various periodicals (such as Expert Systems: The International Journal of Knowledge Engineering, Artificial Intelligence Review, Informatica, and the Journal of Computer Science and Technology) and in conference proceedings (e.g., Research and Development in Expert Systems IX, and the 21st ACM Computer Science Conference). His technical interests include machine learning, expert systems, intelligent database systems, and knowledge-based software engineering. He is an editor on the Editorial Board of the Europe-based Informatica: An International Journal of Computing and Informatics, and a member of the Editorial Board of the U.S.A.-based International Journal of Computers and Their Applications. He has taught courses in Combinatorial Mathematics, Expert Systems, Knowledge Representation and Inference, Machine Learning, Advanced Data Structures and Databases, Introduction to Computer Science, and Artificial Intelligence. PREREQUISITES Databases, Expert Systems, and (preferably) Prolog. ------------------------------------------------------------------------------- Seventh Australian Joint Conference on Artificial Intelligence (AI'94) STRUCTURED SEQUENCE TUTORIALS A structured sequence of pre-conference tutorials on several aspects of applied AI has been organised. Tutorial participants will be able to select from a choice of tutorials to suit their specialist requirements. The following are the list of tutorials organised for AI'94. All participants of any of these tutorials may attend the talk entitled "Introduction to Artificial Intelligence". This is a complementary session for all tutorial participants. Guide: (y) - indicate YES (n) - indicate NO (t) - indicate THEORETICAL SESSION (p) - indicate PRACTICAL SESSION (d) - indicate DEMONSTRATION SESSION 1hr - indicate one hour 4s - indicate four sessions 3s - indicate three sessions Note: Each session is One and a Half hours long. -------------------------------------------------------------------------------- No. Tutorial Title Presenters CODE Length Practical -------------------------------------------------------------------------------- [0] Introduction To Artificial Not Confirmed yet AI 1hr - Intelligence [1] Constraint-Based Reasoning Dr. H. W. Guesgen CBR 4s y [2] Fundamentals of Fuzzy Logic Dr. A. Sekercioglu FLFC 3s y and Fuzzy Logic Controllers G.K. Egan [3] An Introduction to Evolutionary Dr. X. Yao EC 3s y Computation [4] Building Intelligent Decision Dr. J. Zeleznikow IDSS 3s y Support Systems through the use Mr. Dan Hunter of multiple reasonig Strategies [5] Intelligent Learning Database Dr Xindong Wu ILDB 3s y Systems [6] Nonmonotonic Reasoning Dr. M-A Williams NMR 3s y Dr. G. Antoniou [7] Theoretical Foundations of Dr. A. G. Hoffman TFML 3s n Machine Learning Dr. Shyam Kapur Dr. Arun Sharma [8] Knowledge Acquisition and Dr. P. Compton KAM 3s d Maintenance with Ripple Down Rules [9] Hybrid (AI symbolic, Dr. Nik K. Kasabov HS 3s d Connectionist, Fuzzy, Chaotic) Systems -------------------------------------------------------------------------------- Tentative Tutorial Timetable -------------------------------------------------------------------------------- Monday 21/11/94 (Day 1) ======================= 9.30 - 10.30: Introduction to AI 10.30 - 11.00: Morning Tea Break 11.00 - 12.30: NMR(t) IDSS(t) EC(t) TFML(t) 12.30 - 2.00: Lunch 2.00 - 3.30: NMR(t) IDSS(t) EC(t) TFML(t) 3.30 - 4.00: Afternoon Tea Break 4.00 - 5.30: NMR(p) IDDS(p) EC(p) TFML(t) Tuesday 22/11/94 (Day 2) ======================== 9.00 - 10.30: CBR(t) ILDB(t) FLFC(t) KAM(t) HS(t) 10.30 - 11.00: Morning Tea Break 11.00 - 12.30: CBR(t) ILDB(t) FLFC(t) KAM(t) HS(t) 12.30 - 2.00: Lunch 2.00 - 3.30: CBR(t) ILDB(p) FLFC(p) KAM(d) HS(d) 3.30 - 4.00: Afternoon Tea Break 4.00 - 5.30: CBR(p) -------------------------------------------------------------------------------- Examples of structured sequence of tutorials: -------------------------------------------------------------------------------- There are couple of structured sequence of tutorials that one could adopt. For example, if the participant is interested in logic/theoretical basis of AI, then he/she may want to select the following sequence: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: NMR(t) 2.00 - 3.30: NMR(t) 4.00 - 5.30: NMR(p) Day 2 9.00 - 10.30: CBR(t) 11.00 - 12.30: CBR(t) 2.00 - 3.30: CBR(t) 4.00 - 5.30: CBR(p) Alternatively, if the participant is more interested in the applications of AI, then the following sequence may be more suitable: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: IDSS(t) 2.00 - 3.30: IDSS(t) 4.00 - 5.30: IDSS(p) Day 2 9.00 - 10.30: FLFC(t) 11.00 - 12.30: FLFC(t) 2.00 - 3.30: FLFC(p) If the interest is in Machine Learning/Knowledge Acquisition, then the possible sequence may be: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: TFML(t) 2.00 - 3.30: TFML(t) 4.00 - 5.30: TFML(t) Day 2 9.00 - 10.30: ILDB(t) or KAM(t) 11.00 - 12.30: ILDB(t) or KAM(t) 2.00 - 3.30: ILDB(p) or KAM(d) Another possible sequence may be: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: EC(t) 2.00 - 3.30: EC(t) 4.00 - 5.30: EC(p) Day 2 9.00 - 10.30: HS(t) 11.00 - 12.30: HS(t) 2.00 - 3.30: HS(d) -------------------------------------------------------------------------------- Further Information: -------------------------------------------------------------------------------- For further information on the structured sequence tutorials, please contact the AI'94 Tutorial Co-ordinator, at the following address: Dr. Dickson Lukose Department of Mathematics, Statistics, and Computing Science University of New England Armidale, N.S.W., 2351 AUSTRALIA e-mail: ai94@fermat.une.edu.au fax. : (+61 67) 73 3312 -------------------------------------------------------------------------------- Article 22199 of comp.ai: Xref: glinda.oz.cs.cmu.edu comp.ai:22199 comp.ai.genetic:2996 comp.ai.neural-nets:16661 comp.ai.nat-lang:1648 Path: honeydew.srv.cs.cmu.edu!nntp.club.cc.cmu.edu!newsfeed.pitt.edu!godot.cc.duq.edu!news.duke.edu!MathWorks.Com!europa.eng.gtefsd.com!howland.reston.ans.net!pipex!sunic!trane.uninett.no!nac.no!ifi.uio.no!wabbit.cc.uow.edu.au!news.ci.com.au!metro!grivel!fermat.une.edu.au!ai94 From: ai94@fermat.une.edu.au (Artificial Intelligence Conference 1994) Newsgroups: comp.ai,aus.ai,aus.computers.ai,comp.ai.genetic,comp.ai.neural-nets,comp.ai.nat-lang Subject: CFP (AI'94) TUTORIAL ON KNOWLEDGE ACQUISITION AND MAINTENANCE WITH RIPPLE DOWN RULES Message-ID: <4794@grivel.une.edu.au> Date: 18 May 94 07:33:36 GMT Sender: usenet@grivel.une.edu.au Followup-To: ai94@fermat.une.edu.au Lines: 316 Nntp-Posting-Host: fermat.une.edu.au C A L L F O R P A R T I C I P A T I O N S AI'94 Tutorial on Knowledge Acquisition and Maintenance with Ripple Down Rules by P.Compton School of Computer Science and Engineering, University of New South Wales, Sydney 2052, email compton@cse.unsw.edu.au GOALS The aim of this tutorial is to teach participants a simple but powerful knowledge acquisition (KA) technique for building knowledge based systems (KBS), Ripple Down Rules (RDR). The problems in KA arise partly because of our naive cultural expectations as to what an expert is capable of providing. The tutorial will encourage participants to a critical view as to what knowledge acquisition is about and provide a standpoint for understanding and evaluating KA solutions proposed by current research. RDR will be proposed as a partial solution to the KA problem. It is intended that the tutorial will leave participants with a sufficiently detailed understanding of RDR, its strengths and limitations, so that without further KBS background they will be able apply RDR to building KBS. It is intended that, assuming reasonable programming skills, participants will be able to build their own RDR interpreters, adapt existing expert system shells to use RDR, or extend existing information systems to include RDR, if desired. It will be possible to present the algorithms involved in sufficient detail for this because of their simplicity. However, no prior experience will be assumed and participants will be provided with RDR software sufficient to build reasonably large systems. Finally it would be hoped that participants would get a clear idea of the limitations of RDR and perhaps be encouraged to explore solutions to these problems. BACKGROUND The first major problem in the successful development of a KBS is the choice of an application. Assuming the application is suitable, to build a significant KBS requires considerable effort, particularly in acquiring and maintaining the expert knowledge needed by the system. This has been described as the "knowledge engineering bottleneck". The essential problem in KA is that experts never explain how they reach a conclusion, rather they justify that their conclusion is correct. This justification depends on the context in which it is given, whether the person for whom the justification is intended is a lay person, a trainee and expert etc. That is, knowledge is always "situated" in a context. Further, it is not part of an expert's normal skill to be able tocategorise and describe the type of problem solving they carry out. Most current research aims at setting up methodologies to approach these problems in a disciplined and careful way. Such methods, in common with conventional software engineering techniques, are powerful, interesting, have wide applicability, but require sophisticated practitioners. Ripple Down Rules is knowledge acquisition methodology which assumes that since knowledge is given only in a specific context it should be used only in the same context. This leads to an approach to building KBS by incremental refinement covering maintenance as well as initial development. It also allows the knowledge being added to be easily validated. This approach has a number of consequences. Firstly, RDR allow experts to build KBS without any knowledge engineering or programming assistance or training. This has been demonstrated in the PEIRS system at St.Vincent's Hospital Sydney. This systems provides interpretative comments for diagnostic pathology laboratory reports. It currently has over 2000 rules and is 95% correct in the domains it covers. It would seem to be one of the largest medical systems in actual routine use. It also seems to be the only large system in use built without a knowledge engineer (or without induction). Importantly it was put into routine use with only 200 rules and all other knowledge has been added while the system has been in use as a minor extension to the expert's normal duties. Secondly, RDR blur the distinction between maintenance and initial development. The cost of adding a rule with an RDR system is the same regardless of how large the system is and how long it has been under development. The issue of loss of expertise and the difficulty of dealing with a large structure built by someone else does not arise in the incremental approach used with RDR. Thirdly RDR seem to be the first practical approach to KBS based on a situated cognition perspective. Situated cognition is increasingly the basis of a critique of traditional approaches to AI. RDR eschew the notion that it is necessary to search for the best or right way of constructing a KB. Rather knowledge is constructed in context as a consequence of applying insight to a concrete problem. RDR thus emphasise test and repair rather than problem solving and domain analyss. RDR do not immediately solve all problems and have their own intrinsic problems. The most important problem is that the KB may contain repeated knowledge acquired through repeated KA of the same knowledge. Brian Gaines (University of Calgary) has developed an inductive method of building or compressing an RDR KB which will be described in detail. However it has also been shown that this is minor rather than major problem (PEIRS has not yet been compressed). Secondly, RDR were developed for applications where a single classification is required and all the data are available before inference. Extensions to RDR to deal with multiple classifications and problems such as configuration will be described. Participants should expect to gain some insight into how these problems can be dealt with, but the major focus of the tutorial will be on standard single classification RDR. Finally, the development of domain models and suitable data abstraction for RDR will be discussed. TUTORIAL OUTLINE KA problems KBS case study. KA problems and philosophy. Selecting a KBS application Current KA research Ripple Down Rules. Principles, algorithms, knowledge representation. PEIRS. RDR software demonstration Problems and Extensions RDR and Induction. Multiple Classification RDR. Configuration. Data abstraction TUTORIAL MATERIALS Participants will be provided with notes and/or copies of key papers on RDR and a demonstration program for Apple Macintosh or Windows on a PC. The demonstration programs are stand alone and will enable participants to build significant RDR systems. Their best use however will be as a demonstration program as RDR are best used embedded within an information system. The software will be demonstrated during the tutorial. It is not necessary for participants to bring a portable Macintosh or PC to the tutorial, but it will facilitate participation. BIO-DATA OF THE PRESENTER Paul Compton is an Associate Professor in the School of Computer Science and Engineering, University of New South Wales. Before coming to UNSW in 1990, he was Head of Computing Services at the Garvan Institute of Medical Research, Sydney. Together with Kim Horn and Ross Quinlan he was responsible for GARVAN-ES1 one of the first medical expert systems to reach routine use. This system is also one of the very few examples of KBS maintenance as it was continuously maintained over five years. RDR grew out of this maintenance experience. He is a member of the editorial board of the Knowledge Acquisition Journal, has been on the organising committee of various international Knowledge Acquisition Workshops and is a co-chair of the Japanese Knowledge Acquisition Workshop later this year. ------------------------------------------------------------------------------- Seventh Australian Joint Conference on Artificial Intelligence (AI'94) STRUCTURED SEQUENCE TUTORIALS A structured sequence of pre-conference tutorials on several aspects of applied AI has been organised. Tutorial participants will be able to select from a choice of tutorials to suit their specialist requirements. The following are the list of tutorials organised for AI'94. All participants of any of these tutorials may attend the talk entitled "Introduction to Artificial Intelligence". This is a complementary session for all tutorial participants. Guide: (y) - indicate YES (n) - indicate NO (t) - indicate THEORETICAL SESSION (p) - indicate PRACTICAL SESSION (d) - indicate DEMONSTRATION SESSION 1hr - indicate one hour 4s - indicate four sessions 3s - indicate three sessions Note: Each session is One and a Half hours long. -------------------------------------------------------------------------------- No. Tutorial Title Presenters CODE Length Practical -------------------------------------------------------------------------------- [0] Introduction To Artificial Not Confirmed yet AI 1hr - Intelligence [1] Constraint-Based Reasoning Dr. H. W. Guesgen CBR 4s y [2] Fundamentals of Fuzzy Logic Dr. A. Sekercioglu FLFC 3s y and Fuzzy Logic Controllers G.K. Egan [3] An Introduction to Evolutionary Dr. X. Yao EC 3s y Computation [4] Building Intelligent Decision Dr. J. Zeleznikow IDSS 3s y Support Systems through the use Mr. Dan Hunter of multiple reasonig Strategies [5] Intelligent Learning Database Dr Xindong Wu ILDB 3s y Systems [6] Nonmonotonic Reasoning Dr. M-A Williams NMR 3s y Dr. G. Antoniou [7] Theoretical Foundations of Dr. A. G. Hoffman TFML 3s n Machine Learning Dr. Shyam Kapur Dr. Arun Sharma [8] Knowledge Acquisition and Dr. P. Compton KAM 3s d Maintenance with Ripple Down Rules [9] Hybrid (AI symbolic, Dr. Nik K. Kasabov HS 3s d Connectionist, Fuzzy, Chaotic) Systems -------------------------------------------------------------------------------- Tentative Tutorial Timetable -------------------------------------------------------------------------------- Monday 21/11/94 (Day 1) ======================= 9.30 - 10.30: Introduction to AI 10.30 - 11.00: Morning Tea Break 11.00 - 12.30: NMR(t) IDSS(t) EC(t) TFML(t) 12.30 - 2.00: Lunch 2.00 - 3.30: NMR(t) IDSS(t) EC(t) TFML(t) 3.30 - 4.00: Afternoon Tea Break 4.00 - 5.30: NMR(p) IDDS(p) EC(p) TFML(t) Tuesday 22/11/94 (Day 2) ======================== 9.00 - 10.30: CBR(t) ILDB(t) FLFC(t) KAM(t) HS(t) 10.30 - 11.00: Morning Tea Break 11.00 - 12.30: CBR(t) ILDB(t) FLFC(t) KAM(t) HS(t) 12.30 - 2.00: Lunch 2.00 - 3.30: CBR(t) ILDB(p) FLFC(p) KAM(d) HS(d) 3.30 - 4.00: Afternoon Tea Break 4.00 - 5.30: CBR(p) -------------------------------------------------------------------------------- Examples of structured sequence of tutorials: -------------------------------------------------------------------------------- There are couple of structured sequence of tutorials that one could adopt. For example, if the participant is interested in logic/theoretical basis of AI, then he/she may want to select the following sequence: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: NMR(t) 2.00 - 3.30: NMR(t) 4.00 - 5.30: NMR(p) Day 2 9.00 - 10.30: CBR(t) 11.00 - 12.30: CBR(t) 2.00 - 3.30: CBR(t) 4.00 - 5.30: CBR(p) Alternatively, if the participant is more interested in the applications of AI, then the following sequence may be more suitable: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: IDSS(t) 2.00 - 3.30: IDSS(t) 4.00 - 5.30: IDSS(p) Day 2 9.00 - 10.30: FLFC(t) 11.00 - 12.30: FLFC(t) 2.00 - 3.30: FLFC(p) If the interest is in Machine Learning/Knowledge Acquisition, then the possible sequence may be: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: TFML(t) 2.00 - 3.30: TFML(t) 4.00 - 5.30: TFML(t) Day 2 9.00 - 10.30: ILDB(t) or KAM(t) 11.00 - 12.30: ILDB(t) or KAM(t) 2.00 - 3.30: ILDB(p) or KAM(d) Another possible sequence may be: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: EC(t) 2.00 - 3.30: EC(t) 4.00 - 5.30: EC(p) Day 2 9.00 - 10.30: HS(t) 11.00 - 12.30: HS(t) 2.00 - 3.30: HS(d) -------------------------------------------------------------------------------- Further Information: -------------------------------------------------------------------------------- For further information on the structured sequence tutorials, please contact the AI'94 Tutorial Co-ordinator, at the following address: Dr. Dickson Lukose Department of Mathematics, Statistics, and Computing Science University of New England Armidale, N.S.W., 2351 AUSTRALIA e-mail: ai94@fermat.une.edu.au fax. : (+61 67) 73 3312 -------------------------------------------------------------------------------- Article 22200 of comp.ai: Xref: glinda.oz.cs.cmu.edu comp.ai:22200 comp.ai.genetic:2997 comp.ai.neural-nets:16662 comp.ai.nat-lang:1649 Path: honeydew.srv.cs.cmu.edu!nntp.club.cc.cmu.edu!godot.cc.duq.edu!news.duke.edu!eff!usenet.ins.cwru.edu!howland.reston.ans.net!pipex!sunic!trane.uninett.no!nac.no!ifi.uio.no!wabbit.cc.uow.edu.au!news.ci.com.au!metro!grivel!fermat.une.edu.au!ai94 From: ai94@fermat.une.edu.au (Artificial Intelligence Conference 1994) Newsgroups: comp.ai,aus.ai,aus.computers.ai,comp.ai.genetic,comp.ai.neural-nets,comp.ai.nat-lang Subject: CFP (AI'94) TUTORIAL ON NONMONOTONIC REASONING Message-ID: <4795@grivel.une.edu.au> Date: 18 May 94 07:35:31 GMT Sender: usenet@grivel.une.edu.au Followup-To: ai94@fermat.une.edu.au Lines: 265 Nntp-Posting-Host: fermat.une.edu.au C A L L F O R P A R T I C I P A T I O N S AI'94 Tutorial on Nonmonotonic Reasoning by Grigoris Antoniou and Mary-Anne Williams Information Systems Group Dept of Management University of Newcastle, 2308 ga@tyr.informatik.Uni-Osnabrueck.de maryanne@frey.newcastle.edu.au 21 November 1994 ABSTRACT Complete information is difficult to come by and generally not available even in simple database applications. Consequently, an intelligent reasoning system must be capable of making plausible conjectures, which may be retracted when found to be incorrect according to new information that becomes available. Nonmonotonic Reasoning provides a mechanism for an intelligent reasoning system to make such conjectures when its knowledge is incomplete; this is accomplished by sanctioning inferences by default or by determining those that are most likely according to some criteria. The tutorial will consist of a theoretical and a practical component. In the theoretical component we aim to provide a conceptual framework for Nonmonotonic Reasoning. Rather than discussing a very broad set of topics, we will focus on one of the main approaches to Nonmonotonic Reasoning, namely default logic. In particular, we will focus on how one can represent problems and how one may determine extensions within the default logic formalism. In practice, determining extensions is one of the main obstacles when implementing and applying default logic, and is very often neglected in tutorials, courses, and even books. The practical component comprises the hands-on development of an example whose purpose is to illustrate the utility of Nonmonotonic Reasoning. Participants will obtain an appreciation of some methods in the field of Nonmonotonic Reasoning, and will also explore tools and techniques which are very useful in practice. TUTORIAL OUTLINE The topics to be covered are: 1. Nonmonotonic Reasoning: What is it all about? 2. Types of Nonmonotonic Reasoning 2.1 Nonmonotonic inference relations 2.2 Closed world assumption 2.3 Predicate Completion 2.4 Circumscription 2.5 Default Logic 2.6 Autoepistemic Logic 3. Default Logic 3.1 Default rules and theories 3.2 Extensions 3.3 Computing extensions 3.4 Normal default theories 4. Discussion of a prototypical Prolog implementation. 5. Practical session in PC Lab. 6. Summary PREREQUISTES Predicate Calculus, and elementary Prolog. BIO-DATA for the Presenters Grigoris Antoniou studied computer science at the University of Karlsruhe in Germany and earned his Doctorate at the University of Osnabrueck, Germany. He has been working in the Department of Mathematics and Computer Science in Osnabrueck since 1987, firstly as Research Assistant and now as Lecturer. Beginning summer 1994, he will be a Lecturer in Information Systems at the University of Newcastle. His research interests are the logical foundations of computer science and AI. He is a co-author of the book ``Logic: A Foundation of Computer Science'', Addison-Wesley 1991. He has been working on Nonmonotonic Reasoning for the last few years and has several published and forthcoming articles in the area. In the winter semester 1992/93 he gave a course on Nonmonotonic Logic at the University of Osnabrueck. In 1993 he gave a five-hours tutorial at the German Spring School on Artificial Intelligence (KIFS-93). Mary-Anne Williams completed a Bachelor of Science, Diploma in Computer Science and Master of Science at the University of New England, and has recently completed her Doctorate at the University of Sydney. She is a member of the Knowledge Systems Group, a research group in Computer Science at the University of Sydney, and currently works as a Lecturer in the Information Systems Group at the University of Newcastle. Her research interests lie in knowledge representation, nonmonotonic reasoning, belief revision, reasoning about action and explanation, in which she has numerous published articles. She was one of the organisers of the highly successful Workshop on Belief Revision at AI'93 in Melbourne last year. ------------------------------------------------------------------------------- Seventh Australian Joint Conference on Artificial Intelligence (AI'94) STRUCTURED SEQUENCE TUTORIALS A structured sequence of pre-conference tutorials on several aspects of applied AI has been organised. Tutorial participants will be able to select from a choice of tutorials to suit their specialist requirements. The following are the list of tutorials organised for AI'94. All participants of any of these tutorials may attend the talk entitled "Introduction to Artificial Intelligence". This is a complementary session for all tutorial participants. Guide: (y) - indicate YES (n) - indicate NO (t) - indicate THEORETICAL SESSION (p) - indicate PRACTICAL SESSION (d) - indicate DEMONSTRATION SESSION 1hr - indicate one hour 4s - indicate four sessions 3s - indicate three sessions Note: Each session is One and a Half hours long. -------------------------------------------------------------------------------- No. Tutorial Title Presenters CODE Length Practical -------------------------------------------------------------------------------- [0] Introduction To Artificial Not Confirmed yet AI 1hr - Intelligence [1] Constraint-Based Reasoning Dr. H. W. Guesgen CBR 4s y [2] Fundamentals of Fuzzy Logic Dr. A. Sekercioglu FLFC 3s y and Fuzzy Logic Controllers G.K. Egan [3] An Introduction to Evolutionary Dr. X. Yao EC 3s y Computation [4] Building Intelligent Decision Dr. J. Zeleznikow IDSS 3s y Support Systems through the use Mr. Dan Hunter of multiple reasonig Strategies [5] Intelligent Learning Database Dr Xindong Wu ILDB 3s y Systems [6] Nonmonotonic Reasoning Dr. M-A Williams NMR 3s y Dr. G. Antoniou [7] Theoretical Foundations of Dr. A. G. Hoffman TFML 3s n Machine Learning Dr. Shyam Kapur Dr. Arun Sharma [8] Knowledge Acquisition and Dr. P. Compton KAM 3s d Maintenance with Ripple Down Rules [9] Hybrid (AI symbolic, Dr. Nik K. Kasabov HS 3s d Connectionist, Fuzzy, Chaotic) Systems -------------------------------------------------------------------------------- Tentative Tutorial Timetable -------------------------------------------------------------------------------- Monday 21/11/94 (Day 1) ======================= 9.30 - 10.30: Introduction to AI 10.30 - 11.00: Morning Tea Break 11.00 - 12.30: NMR(t) IDSS(t) EC(t) TFML(t) 12.30 - 2.00: Lunch 2.00 - 3.30: NMR(t) IDSS(t) EC(t) TFML(t) 3.30 - 4.00: Afternoon Tea Break 4.00 - 5.30: NMR(p) IDDS(p) EC(p) TFML(t) Tuesday 22/11/94 (Day 2) ======================== 9.00 - 10.30: CBR(t) ILDB(t) FLFC(t) KAM(t) HS(t) 10.30 - 11.00: Morning Tea Break 11.00 - 12.30: CBR(t) ILDB(t) FLFC(t) KAM(t) HS(t) 12.30 - 2.00: Lunch 2.00 - 3.30: CBR(t) ILDB(p) FLFC(p) KAM(d) HS(d) 3.30 - 4.00: Afternoon Tea Break 4.00 - 5.30: CBR(p) -------------------------------------------------------------------------------- Examples of structured sequence of tutorials: -------------------------------------------------------------------------------- There are couple of structured sequence of tutorials that one could adopt. For example, if the participant is interested in logic/theoretical basis of AI, then he/she may want to select the following sequence: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: NMR(t) 2.00 - 3.30: NMR(t) 4.00 - 5.30: NMR(p) Day 2 9.00 - 10.30: CBR(t) 11.00 - 12.30: CBR(t) 2.00 - 3.30: CBR(t) 4.00 - 5.30: CBR(p) Alternatively, if the participant is more interested in the applications of AI, then the following sequence may be more suitable: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: IDSS(t) 2.00 - 3.30: IDSS(t) 4.00 - 5.30: IDSS(p) Day 2 9.00 - 10.30: FLFC(t) 11.00 - 12.30: FLFC(t) 2.00 - 3.30: FLFC(p) If the interest is in Machine Learning/Knowledge Acquisition, then the possible sequence may be: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: TFML(t) 2.00 - 3.30: TFML(t) 4.00 - 5.30: TFML(t) Day 2 9.00 - 10.30: ILDB(t) or KAM(t) 11.00 - 12.30: ILDB(t) or KAM(t) 2.00 - 3.30: ILDB(p) or KAM(d) Another possible sequence may be: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: EC(t) 2.00 - 3.30: EC(t) 4.00 - 5.30: EC(p) Day 2 9.00 - 10.30: HS(t) 11.00 - 12.30: HS(t) 2.00 - 3.30: HS(d) -------------------------------------------------------------------------------- Further Information: -------------------------------------------------------------------------------- For further information on the structured sequence tutorials, please contact the AI'94 Tutorial Co-ordinator, at the following address: Dr. Dickson Lukose Department of Mathematics, Statistics, and Computing Science University of New England Armidale, N.S.W., 2351 AUSTRALIA e-mail: ai94@fermat.une.edu.au fax. : (+61 67) 73 3312 -------------------------------------------------------------------------------- Article 22201 of comp.ai: Xref: glinda.oz.cs.cmu.edu comp.ai:22201 comp.ai.genetic:2998 comp.ai.neural-nets:16663 comp.ai.nat-lang:1650 Path: honeydew.srv.cs.cmu.edu!nntp.club.cc.cmu.edu!newsfeed.pitt.edu!godot.cc.duq.edu!news.duke.edu!eff!usenet.ins.cwru.edu!howland.reston.ans.net!pipex!sunic!trane.uninett.no!nac.no!ifi.uio.no!wabbit.cc.uow.edu.au!news.ci.com.au!metro!grivel!fermat.une.edu.au!ai94 From: ai94@fermat.une.edu.au (Artificial Intelligence Conference 1994) Newsgroups: comp.ai,aus.ai,aus.computers.ai,comp.ai.genetic,comp.ai.neural-nets,comp.ai.nat-lang Subject: CFP (AI'94) TUTORIAL ON THEORETICAL FOUNDATIONS OF MACHINE LEARNING Message-ID: <4796@grivel.une.edu.au> Date: 18 May 94 07:37:34 GMT Sender: usenet@grivel.une.edu.au Followup-To: ai94@fermat.une.edu.au Lines: 276 Nntp-Posting-Host: fermat.une.edu.au C A L L F O R P A R T I C I P A T I O N S AI'94 Tutorial on Theoretical Foundations of Machine Learning (Full-day Tutorial on 21 Nov 94) by Dr Achim Hoffmann School of Computer Science and Engineering The University of New South Wales Sydney, NSW, 2052, Australia achim@cse.unsw.edu.au Dr Shyam Kapur Dept. of Computer Science, James Cook University, Townsville, Queensland 4811, Australia kapur@coral.cs.jcu.edu.au Dr Arun Sharma School of Computer Science and Engineering The University of New South Wales Sydney, NSW, 2052, Australia arun@cse.unsw.edu.au ABSTRACT The act of learning is one of the most important hallmarks of what it means to be intelligent and most AI systems are far from exhibiting this property. Realization of this deficiency in expert systems has caused learning to become a central issue in AI today. Herbert Simon, in his keynote address at last year's AAAI, noted that Machine Learning will increasingly take center stage in AI throughout the next decade. Most future AI systems will need some ability to learn and to adapt to changes in their environments unforeseen by the system developers. Machine Learning certainly is one of the hardest problems in AI today. Despite considerable progress, it can be argued that some approaches to Machine Learning are still practiced as ``black art.'' Sometimes a learning algorithm achieves astonishing results; and yet at other times they perform so poorly that the user ends up tinkering with the system without any reasonable return for their effort. Fundamental investigations into the problems and challenges of Machine Learning have been conducted in Philosophy for ages, and more recently such studies have also been conducted in Computer Science. GOALS OF THE TUTORIAL 1. To clearly lay down the central conceptual ingredients of a learning framework. These ingredients are not clearly distinguished in many practical approaches. 2. An introduction to the mathematical analysis of learning tasks and the basic techniques of Computational Learning Theory. 3. The interplay between learning algorithm, training data, computing time and success criteria will be explored. DETAILED PLAN 1. Computational Limitations on Learning 1.1 Learnability from positive data. 1.2 Learnability from positive and negative data. 1.3 Batch and Incremental Learning. 2. Heuristics for Learning 2.1 Monotonic Heuristics. 2.2 Consistent and Inconsistent Heuristics 2.3 Pattern Languages. 3. Resource Requirements for Learning 3.1 Introduction to probably approximately correct learning 3.2 Bounds on required number of training examples. 3.3 Computational Complexity of Learning Tasks. 3.4 Analysis of Neural Network's Learning Power. COURSE MATERIAL: Lecture notes and bibliographical references will be provided for every participant. BIO-DATA OF THE PRESENTERS Dr. Dr. ACHIM G. HOFFMANN received his PhD in Computer Science in 1992 from Technische Universitaet Berlin (West). 1993 he received his PhD in Philosophy from the same institution. Since January 1993 he is Lecturer at UNSW. His current research interests include Machine Learning, Neural Networks, Computational Learning Theory and the Philosophical Foundations of AI. He has published some 20 papers in Journals (e.g. Journal of Experimental and Theoretical AI, Neurocomputing - An International Journal) and Conference Proceedings (e.g. IJCAI, ECAI, IJCNN) in the areas of Design Automation, Machine Learning, and the Philosophical Foundations of AI. Dr. SHYAM KAPUR received his Ph.D. also in Computer Science from Cornell University. He was a Post Doctoral Research Fellow at the Institute for Research in Cognitive Science (University of Pennsylvania) from 1991 to 1993. He has published about 20 papers in journals (such as Theoretical Computer Science and Cognition), in book volumes, and in conference proceedings (COLT, ALT). His technical interests include computational learning theory and computational linguistics especially as they pertain to the cognitive modeling of both natural language acquisition and processing. He has taught introductory classes in computer science and an honours-level class in artificial intelligence. Dr. ARUN SHARMA received his PhD in Computer Science from the State University of New York at Buffalo. Prior to joining the School of Computer Science and Engineering at UNSW, he was a post-doctoral associate in the Department of Brain and Cognitive Sciences at the MIT. He has published over twenty five articles in journals (e.g., Information and Computation, Journal of Computer and System Sciences, and Theoretical Computer Science) and conferences (e.g., COLT, ALT, and ICALP). His technical interests include Learning Theory, Machine Learning, and Computability and Complexity. ------------------------------------------------------------------------------- Seventh Australian Joint Conference on Artificial Intelligence (AI'94) STRUCTURED SEQUENCE TUTORIALS A structured sequence of pre-conference tutorials on several aspects of applied AI has been organised. Tutorial participants will be able to select from a choice of tutorials to suit their specialist requirements. The following are the list of tutorials organised for AI'94. All participants of any of these tutorials may attend the talk entitled "Introduction to Artificial Intelligence". This is a complementary session for all tutorial participants. Guide: (y) - indicate YES (n) - indicate NO (t) - indicate THEORETICAL SESSION (p) - indicate PRACTICAL SESSION (d) - indicate DEMONSTRATION SESSION 1hr - indicate one hour 4s - indicate four sessions 3s - indicate three sessions Note: Each session is One and a Half hours long. -------------------------------------------------------------------------------- No. Tutorial Title Presenters CODE Length Practical -------------------------------------------------------------------------------- [0] Introduction To Artificial Not Confirmed yet AI 1hr - Intelligence [1] Constraint-Based Reasoning Dr. H. W. Guesgen CBR 4s y [2] Fundamentals of Fuzzy Logic Dr. A. Sekercioglu FLFC 3s y and Fuzzy Logic Controllers G.K. Egan [3] An Introduction to Evolutionary Dr. X. Yao EC 3s y Computation [4] Building Intelligent Decision Dr. J. Zeleznikow IDSS 3s y Support Systems through the use Mr. Dan Hunter of multiple reasonig Strategies [5] Intelligent Learning Database Dr Xindong Wu ILDB 3s y Systems [6] Nonmonotonic Reasoning Dr. M-A Williams NMR 3s y Dr. G. Antoniou [7] Theoretical Foundations of Dr. A. G. Hoffman TFML 3s n Machine Learning Dr. Shyam Kapur Dr. Arun Sharma [8] Knowledge Acquisition and Dr. P. Compton KAM 3s d Maintenance with Ripple Down Rules [9] Hybrid (AI symbolic, Dr. Nik K. Kasabov HS 3s d Connectionist, Fuzzy, Chaotic) Systems -------------------------------------------------------------------------------- Tentative Tutorial Timetable -------------------------------------------------------------------------------- Monday 21/11/94 (Day 1) ======================= 9.30 - 10.30: Introduction to AI 10.30 - 11.00: Morning Tea Break 11.00 - 12.30: NMR(t) IDSS(t) EC(t) TFML(t) 12.30 - 2.00: Lunch 2.00 - 3.30: NMR(t) IDSS(t) EC(t) TFML(t) 3.30 - 4.00: Afternoon Tea Break 4.00 - 5.30: NMR(p) IDDS(p) EC(p) TFML(t) Tuesday 22/11/94 (Day 2) ======================== 9.00 - 10.30: CBR(t) ILDB(t) FLFC(t) KAM(t) HS(t) 10.30 - 11.00: Morning Tea Break 11.00 - 12.30: CBR(t) ILDB(t) FLFC(t) KAM(t) HS(t) 12.30 - 2.00: Lunch 2.00 - 3.30: CBR(t) ILDB(p) FLFC(p) KAM(d) HS(d) 3.30 - 4.00: Afternoon Tea Break 4.00 - 5.30: CBR(p) -------------------------------------------------------------------------------- Examples of structured sequence of tutorials: -------------------------------------------------------------------------------- There are couple of structured sequence of tutorials that one could adopt. For example, if the participant is interested in logic/theoretical basis of AI, then he/she may want to select the following sequence: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: NMR(t) 2.00 - 3.30: NMR(t) 4.00 - 5.30: NMR(p) Day 2 9.00 - 10.30: CBR(t) 11.00 - 12.30: CBR(t) 2.00 - 3.30: CBR(t) 4.00 - 5.30: CBR(p) Alternatively, if the participant is more interested in the applications of AI, then the following sequence may be more suitable: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: IDSS(t) 2.00 - 3.30: IDSS(t) 4.00 - 5.30: IDSS(p) Day 2 9.00 - 10.30: FLFC(t) 11.00 - 12.30: FLFC(t) 2.00 - 3.30: FLFC(p) If the interest is in Machine Learning/Knowledge Acquisition, then the possible sequence may be: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: TFML(t) 2.00 - 3.30: TFML(t) 4.00 - 5.30: TFML(t) Day 2 9.00 - 10.30: ILDB(t) or KAM(t) 11.00 - 12.30: ILDB(t) or KAM(t) 2.00 - 3.30: ILDB(p) or KAM(d) Another possible sequence may be: Day 1 9.30 - 10.30: Introdution to AI 11.00 - 12.30: EC(t) 2.00 - 3.30: EC(t) 4.00 - 5.30: EC(p) Day 2 9.00 - 10.30: HS(t) 11.00 - 12.30: HS(t) 2.00 - 3.30: HS(d) -------------------------------------------------------------------------------- Further Information: -------------------------------------------------------------------------------- For further information on the structured sequence tutorials, please contact the AI'94 Tutorial Co-ordinator, at the following address: Dr. Dickson Lukose Department of Mathematics, Statistics, and Computing Science University of New England Armidale, N.S.W., 2351 AUSTRALIA e-mail: ai94@fermat.une.edu.au fax. : (+61 67) 73 3312 -------------------------------------------------------------------------------- Article 22202 of comp.ai: Xref: glinda.oz.cs.cmu.edu comp.ai:22202 comp.ai.genetic:2999 comp.ai.neural-nets:16664 comp.ai.nat-lang:1651 Path: honeydew.srv.cs.cmu.edu!nntp.club.cc.cmu.edu!newsfeed.pitt.edu!godot.cc.duq.edu!news.duke.edu!eff!usenet.ins.cwru.edu!howland.reston.ans.net!pipex!sunic!trane.uninett.no!nac.no!ifi.uio.no!wabbit.cc.uow.edu.au!news.ci.com.au!metro!grivel!fermat.une.edu.au!ai94 From: ai94@fermat.une.edu.au (Artificial Intelligence Conference 1994) Newsgroups: comp.ai,aus.ai,aus.computers.ai,comp.ai.genetic,comp.ai.neural-nets,comp.ai.nat-lang Subject: AI'94 ELECTRONIC INFORMATION SERVER Message-ID: <4797@grivel.une.edu.au> Date: 18 May 94 07:40:38 GMT Sender: usenet@grivel.une.edu.au Followup-To: ai94@fermat.une.edu.au Lines: 54 Nntp-Posting-Host: fermat.une.edu.au Seventh Australian Joint Conference on Artificial Intelligence (AI'94) E L E C T R O N I C I N F O R M A T I O N In conjunction with the Department of Mathematics, Statistics and Computing Science the Organising Committee has arranged for regular updates of information relating to the conference to be made electronically available. There are currently four ways in which information relating to the conference may be accessed: 1. Anonymous ftp fermat.une.edu.au:/pub/ai94 2. Via the ai94info mail server ai94-info is set up to respond to requests for files and information relating to the AI94 conference. To retrieve information on a particular topic such as the conference programme simply mail ai94-info with your request. ie %mail -s "send workshops" ai94-info@fermat.une.edu.au requests for more than one file may be made by including additional send commands (one per line) within the body of the mail. All information at the site will be updated on a monthly basis. For a complete listing of the files/topics which are currently available request the "index" file. 3. Via gopher (or xgopher) Name: UNE Gopher Server Type: 1 Host: fermat.une.edu.au Port: 70 4. Via mosaic (or other WWW browser) URL: http://fermat.une.edu.au If you experience any problem with any of the above services please do not hesitate to send email to ai94@fermat.une.edu.au with a description of the problem you are experiencing. Article 22206 of comp.ai: Xref: glinda.oz.cs.cmu.edu comp.ai:22206 comp.ai.genetic:3007 comp.ai.neural-nets:16674 comp.ai.nat-lang:1652 Path: honeydew.srv.cs.cmu.edu!nntp.club.cc.cmu.edu!newsfeed.pitt.edu!gatech!howland.reston.ans.net!pipex!uknet!EU.net!sunic!trane.uninett.no!nac.no!ifi.uio.no!wabbit.cc.uow.edu.au!news.ci.com.au!metro!grivel!fermat.une.edu.au!ai94 From: ai94@fermat.une.edu.au (Artificial Intelligence Conference 1994) Newsgroups: comp.ai,aus.ai,aus.computers.ai,comp.ai.genetic,comp.ai.neural-nets,comp.ai.nat-lang Subject: CFP (AI'94) POST-GRADUATE STUDENT SESSION Message-ID: <4847@grivel.une.edu.au> Date: 18 May 94 12:51:20 GMT Sender: usenet@grivel.une.edu.au Followup-To: ai94@fermat.une.edu.au Lines: 135 Nntp-Posting-Host: fermat.une.edu.au Seventh Australian Joint Conference on Artificial Intelligence (AI'94) "Sowing the Seeds for the Future" POST-GRADUATE STUDENT SESSION A unique opportunity for post-graduate students in AI to discuss their research with eminent scientists. CALL FOR PARTICIPATION Conference dates: 21 - 25 November 1994 Proudly sponsored by Microsoft Institute (principal sponsor), IBM, Sun Microsystems, Australian Computer Society, and Department of Mathematics, Statistics, and Computing Science (UNE). Hosted by Department of Mathematics, Statistics, and Computing Science The University of New England Armidale, N.S.W., 2351 AUSTRALIA The Conference -------------- AI'94 is the Seventh Australian Joint Conference on Artificial Intelligence. AI'94 is conducted under the auspices of the Australian Computer Society's National Committee for Artificial Intelligence and Expert Systems. The theme of the conference is "Sowing the Seeds for the Future". AI'94 will be hosted by The Department of Mathematics, Statistics, and Computing Science at The University of New England, between Monday 21st November and Friday 25th November 1994. The Post-Graduate Students Session ---------------------------------- A pre-conference Post-Graduate Students Session is scheduled on the 22nd November 1994. This session is specifically organised for all the Post-Graduate Research Students in Australian Universities, who are doing research in Artificial Intelligence. The AI'94 Organising Committee has secured three world renowned scientists in Artificial Intelligence, as keynote speakers. We have also arranged for them to participate on panel sessions in the Post-Graduate Students Session. All participating students will get the opportunity to discuss various aspects of their research and find out the latest developments in Artificial Intelligence, in an informal atmosphere with these three keynote speakers. This will provide a unique opportunity for research students to discuss their work with internationally renowned scientists. The Keynote Speakers -------------------- Professor Wolfgang Wahlster, University of Saarbruecken, Germany. Professor Wahlster is a Professor of Artificial Intelligence in the Department of Computer Science at the University of Saarbruecken, Germany where he currently serves as a Scientific Director of the German Research Center for Artificial Intelligence (DFKI). Since 1975 he has been working in the field as a principal investigator in various language projects, including HAM-ANS, WISBER, SC, XTRA, VITRA and WIP. He has published more than 100 technical papers on natural language processing. His current research includes intelligent multimodal interfaces, user modeling, natural language scene description, intelligent help systems, and deductive plan recognition and generation. Prof. Wahlster is on the editorial boards of various international journals and book series such as Artificial Intelligence, Applied Artificial Intelligence, User Modeling and User-adapted Interaction, Symbolic Computation and the MIT-ACL series. He is a AAAI Fellow and a recipient of the Fritz Winter Award, one of the most prestigious awards for engineering sciences in Germany, for his research on cooperative user interfaces. Prof. Wahlster served as the Conference Chair for IJCAI-93 in Chambery and the Chair of the Board of Trustees of IJCAII from 1991 -1993. Professor Katia Sycara, Carnegie Mellon University, USA. Professor Sycara is a Research Scientist in the School of Computer Science at Carnegie Mellon University. She is also Director of the Enterprise Integration Laboratory. She is directing and conducting research aimed at developing decision support systems for integrating organisational decision making. Her doctoral research contributed to the definition of the case-based reasoning paradigm. She has been Principal Investigator of various government and industry funded research (e.g. distributed scheduling, concurrent engineering, enterprise integration, case-based engineering design, crisis action planning). Prof. Sycara is the author of a book on manufacturing and over 70 technical papers dealing with negotiation, distributed problem solving, case-based reasoning, integration of case-based reasoning with other problem solving methods, and constraint-based reasoning. She is the Area Editor for AI and Management Science for the journal "Group Decision and Negotiation" and on the editorial board of "AI in Engineering" and "Concurrent Engineering: Research and Applications". She is a member of AAAI, ACM, IEEE, and the Institute for Management Science (TIMS). Professor John F. Sowa, State University of New York, USA. Professor Sowa is the author of the book Conceptual Structures, which in the past ten years has led to a world-wide movement of people who are using, implementing, and extending the theory of conceptual graphs. He had been working at IBM for 30 years on various aspects of computer systems design and development, especially artificial intelligence and computational linguistics. Now, he is teaching, writing, and working on standards for conceptual schemas with the American National Standards Institute (ANSI) and the International Standards Organization (ISO). Invitation to Participate ------------------------- Only Post-Graduate students attending the conference are invited to this special session. If you would like to participate in this session, please email an one-page abstract which: * describes your research in Artificial Intelligence and * lists the issues you would like to raise to the following electronic mail address, not later than 1st October 1994: debenham@socs.uts.edu.au All abstracts received will be forwarded to those who respond. Correspondence -------------- All enquires regarding Post-Graduate Student Session (AI'94) should be address to: Professor John Debenham School of Computing Sciences University of Technology Sydney, N.S.W., 2007 AUSTRALIA E-mail: debenham@socs.uts.edu.au Fax: +61-2-330 1807