From ai94@fermat.une.edu.au Mon Dec 13 19:41:54 EST 1993 Article: 5258 of news.announce.conferences Xref: glinda.oz.cs.cmu.edu news.announce.conferences:5258 Newsgroups: news.announce.conferences Path: honeydew.srv.cs.cmu.edu!nntp.club.cc.cmu.edu!news.sei.cmu.edu!cis.ohio-state.edu!magnus.acs.ohio-state.edu!usenet.ins.cwru.edu!howland.reston.ans.net!cs.utexas.edu!uunet!sparky!rick From: ai94@fermat.une.edu.au (Artificial Intelligence Conference 1994) Subject: Electronic Information: 7th Australian Joint Conference on Artificial Intelligence Message-ID: <1993Dec10.145337.8370@sparky.sterling.com> Followup-To: ai94@fermat.une.edu.au Sender: rick@sparky.sterling.com (Richard Ohnemus) Organization: Sterling Software Date: Fri, 10 Dec 1993 14:53:37 GMT Approved: rick@sparky.sterling.com Expires: Sat, 26 Nov 1994 08:00:00 GMT Lines: 52 X-Md4-Signature: 8392a091f839fa000241a05aab5af6a6 ================== FIRST ANNOUNCEMENT ================== ELECTRONIC INFORMATION Seventh Australian Joint Conference on Artificial Intelligence (AI'94) "Sowing the Seeds for the Future" 21 - 25 November 1994 Hosted by Department of Mathematics, Statistics, and Computing Science The University of New England Armidale, N.S.W., 2351 AUSTRALIA 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 xmossaic (or other WWW browser) URL: http://fermat.une.edu.au:70 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 5264 of news.announce.conferences: Xref: glinda.oz.cs.cmu.edu news.announce.conferences:5264 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!gatech!europa.eng.gtefsd.com!uunet!sparky!rick From: ai94@neumann.une.edu.au (Artificial Intelligence Conference 1994) Subject: CFP: 7th Australian Joint Conference on Artificial Intelligence Message-ID: <1993Dec10.145343.8443@sparky.sterling.com> Followup-To: ai94@fermat.une.edu.au Keywords: Call for Papers Sender: rick@sparky.sterling.com (Richard Ohnemus) Organization: Sterling Software Date: Fri, 10 Dec 1993 14:53:43 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 23922 of comp.ai: Path: cantaloupe.srv.cs.cmu.edu!das-news.harvard.edu!news2.near.net!MathWorks.Com!yeshua.marcam.com!zip.eecs.umich.edu!newsxfer.itd.umich.edu!isclient.merit.edu!msuinfo!harbinger.cc.monash.edu.au!news.cs.su.oz.au!metro!grivel!fermat.une.edu.au!ai94 From: ai94@fermat.une.edu.au (Artificial Intelligence Conference 1994) Newsgroups: aus.computers.ai,aus.ai,comp.ai,comp.ai.genetic,comp.ai.neural-nets Subject: (Australia) (AI'94) Registration Form Message-ID: <6576@grivel.une.edu.au> Date: 26 Aug 94 00:31:27 GMT Sender: news@grivel.une.edu.au Followup-To: poster Lines: 664 Nntp-Posting-Host: fermat.une.edu.au Xref: glinda.oz.cs.cmu.edu comp.ai:23922 comp.ai.genetic:3734 comp.ai.neural-nets:18565 Seventh Australian Joint Conference on Artificial Intelligence (AI'94) "Sowing the Seeds for the Future" 21st - 25th November 1994 University of New England Armidale, N.S.W. AUSTRALIA AN INVITATION TO ATTEND AI'94 AI'94 is the Seventh Australian Joint Conference on Artificial Intelligence which will be hosted by The University of New England, Armidale, between Monday 21st November and Friday 25th November 1994. On behalf of the programme and organising committees, we are very pleased to invite you to attend AI'94. This year we have received 152 papers submitted to AI'94, of which 75 papers (49.3% of 152) came from a total of 18 overseas countries. The conference program will consist of 9 Tutorials, 5 Workshops, and 1 Postgraduate Students Session on the Monday 21st and Tuesday 22nd November, and technical paper presentation sessions from Wednesday 23rd to Friday 25th of November. There are three Keynote addresses from renowned international speakers. Professor Wolfgang Wahlster from the German Research Centre for AI (DFKI) will address "Intellimedia: Planning Language, Graphics and Layout for Adaptive Information Presentation". He is a AAAI Fellow and 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 from Carnegie Mellon University (CMU) will address "The Present and Future of Distributed Artificial Intelligence". She is well known for her work on distributed artificial intelligence and integration of case-based reasoning with other problem solving methods. Professor John F. Sowa from State University of New York - Binghamton will address "Sharing and Integrating Knowledge Bases". He 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. These three keynote speakers will also attend the postgraduate session to discuss various AI problems with attendants. We are sure that your attendance will benefit both AI'94 and yourself. We are looking forward to meeting you in Armidale in November 1994. Dr. Chengqi Zhang Dr. Dickson Lukose Prof. John Debenham AI'94 Programme Committee Co-Chairs AI'94 Organising Committee Chair IMPORTANT DATES Camera-Ready Papers Due: 22nd August 1994 Last Day for "Early Bird Registration": 1st October 1994 Conference: 21st - 25th November 1994 ENQUIRES TO Conference Secretary: - Allan Williams Postal Address: AI'94 Conference Secretary Department of Mathematics, Statistics and Computing Science University of New England Armidale, NSW 2351 Australia Telephopne: +61 67 73 2298 or 73 2412 Facsimile: +61 67 73 3312 Email: ai94@fermat.une.edu.au SPONSORS The Organisers of AI'94 would like to acknowledge the support of the following sponsors: Mircrosoft Institute (Principal Sponsor for the conference) IBM Australia 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 Department of Mathematics, Statistics and Computng Science - UNE Sun Microsystems GENERAL REGISTRATION INFORMATION Tutorial Registration includes entry to the Introduction to AI tutorial on Monday 21st and the following morning tea. Entry to nominated tutorial(s), copies of associated teaching material, appropriate morning and afternoon teas and lunch on the day of the nominated tutorial.(Please enter the required tutorial codes when filling in registration form) Workshop Registration includes entry to nominated workshop, morning and afternoon teas as well as lunch on the day of the workshop. NOTE: Participation in a specific workshop must be approved by the workshop organisers. (Please enter workshop code on registration form) Full Registration includes entry to all sessions, conference proceedings in a satchel, morning and afternoon teas, temporary sports union membership, welcome BBQ and conference dinner. Student Registration is available to full-time students at tertiary institutions. It includes entry to all sessions, conference proceedings in a satchel, morning and afternoon teas, temporary sports union membership and welcome BBQ. Student registration does NOT include the conference dinner. For student registration we require certification of full-time student status in the form of a letter from your Head of Department or your supervisor. Day Registration includes entry to all sessions, morning and afternoon teas on the one day. CANCELLATION POLICY Cancellation must be notified in writing to the conference secretary. Cancellations received before 28th October will not incur a cancellation fee. Cancellations received after this date will be subject to a cancellation fee of $200 and a copy of the proceedings would be mailed. This policy does not apply to authors whose papers are selected for publication. REGISTRATION PAYMENTS Registration forms and payments should be posted to: AI'94 Conference Secretary Department of Maths, Stats & Comp. Sci. University of New England Armidale NSW 2351 Australia Delegates paying via credit card may fax or email their registration details direct to the conference secretary. TRAVEL International: Overseas delegates are reminded that most visitors to Australia will need a visa for entry. It is the delegates responsibility to check with their local travel agent or Australian consulate as to how to arrange for the visa. Domestic Airlines: Ansett has been appointed the official carrier for all domestic air travel. When making booking arrangements delegates should quote the following masterfile number: MC01523 Group discounts of 35% off the regular fare is also available. To contact the nearest Ansett Travel Consultant ring: 13 14 13 from anywhere in Australia Sydney - Armidale: Ansett Airlines do not fly direct to Armidale. As a consequence, a seperate conference code is required for Hazelton's Airlines: YO CONF 15 (Quoting the above code will entitle you to 15% discount off the regular full airfare.) Road: Armidale is located on the New England Highway (No.15) and all major bus companies have a service which passes through Armidale. Alternatively delegates may choose to drive from either Sydney or Brisbane. Typically it is a 6.5 hours car trip from Sydney or 5.5 hours by car from Brisbane. Rail: Armidale is serviced by the NSW Railways and is located at the Northern End of the Explorer Rail Service. To and from the Conference: There is a single bus company which runs a bus service between the centre of town and the University. The cost for a one way trip is $1.50. Due to the limited route of the buses Taxi is the only other public transport available. On average you can expect to pay approximately $10 from the motels listed below to the University. ACCOMMODATION BOOKINGS The organising committee has obtained a bed, breakfast package for $43 per night for conference participants who choose to stay at Mary White College (MWC). MWC is located only 2 mins walk from all conference venues. The organising committee believes that the accommodation at MWC offers the best value for delegates attending the conference. MWC college offers a typical college style accommodation with a single study bedroom, telephone, linen, towels and shared bathroom facilities. As added incentive the College has agreed to provide evening meals for those people staying at MWC on Wednesday Night and for students on Thursday night. Delegates wishing to stay at MWC are asked to pay for their accommodation in advance along with their registration to AI'94. The Organising Committee understands that a college type of accommodation is not suitable for or desirable for all delegates and so has included a list of alternate accommodation possibilities. Accommodation providers which provide some evening meals are also marked. Prices and Ratings listed are taken from the NRMA Accommodation guide and are provided only as an indication to the price of a double room and standard of the accommodation available. The organising committee will book accommodation of your choice where possible but it will be the responsibility of the delegate to pay for thier accommodation on departure. CONFIRMATION OF BOOKINGS Registration forms and fees will be acknowledged and MWC accommodation bookings confirmed, as they are received. Please check the confirmation letter and advise of any changes immediately. ALTERNATE ACCOMMODATION OPTIONS Name Address Tel. Fax. Price Meals +61 67 **** Armidale Cattleman's Motor 31 Marsh Street 72 7788 71 1447 $95-$115 Y Inn Armidale Regency Motor Inn 208 Dangar Street 72 9800 71 2590 $64-$88 Y Homefields Deer Park Motor 72-74 Glenn Innes Rd 72 9999 72 8962 $54-$76 Y Inn ***.5 Alluna Motel 180 Dangar Street 72 6262 72 6262 $48-$64 Y Cedar Lodge 119 Barney Street 72 9511 72 9516 $58-$70 Club Motel 105-107 Dumaresq St 72 8777 72 8669 $68-$79 Costwold Gardens Motor Inn 34 Marsh Street 72 8222 72 5139 $59-$74 Y New England Motor Inn 100 Dumaresq Street 71 1011 $58-$70 Sandstock Motor Inn 101 Dumaresq Street 72 9988 72 8490 $60-$80 Y Westwood Motor Inn 62 Barney Street 72 8000 $55-$65 Y White Lanterns Motel 22 Marsh Street 72 5777 $52-$69 Y *** Abbotsleigh Motor Inn 76 Barney Street 72 9488 72 7066 $56-$78 Y Acacia Motor Inn 192 Miller Street 72 7733 71 1901 $54-$60 Armidale Acres Motel and New England Highway 71 1281 $55 Y Caravan Park Armidale Motel 66 Glenn Innes Road 72 8122 72 1024 $48-$68 Y Cameron Lodge Motor Inn Cnr Dangar&Barney St. 72 2351 72 5600 $68 Y Country Comfort Motel 86 Barney Street 72 8511 72 7535 $75 Y Kramer Motor Inn 113 Barney Street 72 5200 $50-$55 Y Hideaway Motor Inn 70 Glen Innes Road 72 5177 71 2609 $62-$76 Y Moore Park Motor Inn New England Highway 72 2358 72 5252 $79 Y **.5 Rosevilla Motel New England Highway 72 8772 72 3872 $42-$45 Y *.5 Tattersall's Hotel The Mall, Beardy St. 72 2247 72 7781 $38-$44 No Rating Available Highlander Van Village New England Highway 72 4768 Pembroke Caravan Park Grafton Road 72 6470 Royal Hotel Cnr Marsh & Beardy St 72 2259 St Kilda Hotel Cnr Marsh & Rusden St 72 4459 Wicklow Hotel(Backpackers) Cnr Marsh & Dumaresq St. 72 2421 Cherrybrook Cottage 178 Allingham Street 72 4222 Comeytrowe 184 Marsh Street 72 5869 $70 Monivea 172 Brown Street 72 8001 Poppy's Cottage Dangarsleigh Road 75 1277 72 8290 $60 Hedgerow Farm PO Box 865 72 1612 $85 TUTORIAL SESSIONS AVAILABLE Monday, 21st November Code Length Title Presenter(s) Prac. Cost* AI 1hr Introduction to Artificial Prof. James Alty No Free** Intelligence NMR 3s Nonmonotonic Reasoning Dr. M-A Williams Yes $300 Dr. G. Antoniou IDSS 3s Building Intelligent Dr. J. Zeleznikow Yes $300 Decision Support Mr. Dan Hunter Systems EC 3s An Introduction to Dr. X. Yao Yes $300 Evolutionary Computation Prof. Z. Michalewicz TFML 3s Theoretical Foundations of Dr. A. G. Hoffman No $300 Machine Learning Dr. Shyam Kapur Dr. Arun Sharma Tuesday, 22nd November Code Length Title Presenter(s) Prac. Cost* CBR 4s Constraint-Based Reasoning Dr. H. W. Guesgen Yes $350 ILDB 3s Intelligent Learning Dr Xindong Wu Yes $300 Database Systems FLFC 3s Fundamentals of Fuzzy Dr. A. Sekercioglu Yes $300 Logic and Fuzzy Logic Mr. G. K. Egan Controllers KAM 3s Knowledge Acquisition Dr. P. Compton Demo $250 and Maintenance with Ripple Down Rules HS 3s Hybrid (AI symbolic, Dr. Nik K. Kasabov Demo $250 Connectionist, Fuzzy, Chaotic) Systems *If attending the technical sessions of the conference, subtract $100 from cost of each tutorial. **Free if enrolled in any other tutorial The cost for student attendance at tutorials will be 50% of the calculated cost for a full delegates. 4s = Full day, 3s = 3/4 day Further information on the listed tutorials is available electronically or by contacting the Conference Secretary. WORKSHOP SESSIONS Code: CS Title: 1st Australian Conceptual Structures Workshop Contact: Mr. Gerard Ellis Email: ged@cs.rmit.edu.au Important Dates: Submission Deadline August 31, 1994. Notification of Acceptance September 30, 1994. Camera-ready copy October 15, 1994. Workshop November 22, 1994. Code: ECW Title: AI'94 Workshop on Evolutionary Computation Contact: Dr. X. Yao Email: xin@csadfa.cs.adfa.oz.au Important Dates: Submission Deadline August 8, 1994. Notification of Acceptance September 12, 1994. Camera-ready copy October 17, 1994. Workshop November 21 & 22, 1994 (1.5 days). Code: ES Title: AI'94 Workshop on Expert Systems in Production use Contact: Dr. Eric Tsui Email: eric@cs.su.oz.au Important Dates: Abstract Submission Deadline August 31, 1994. Notification of Acceptance September 15, 1994. Workshop November 22, 1994. Code: KBNR Title: AI'94 Workshop on Knowledge-Based Systems in Natural Resource Management Contact: Dr. John Weckert Email: weckert@csu.edu.au Important Dates: Abstract Submission Deadline August 31, 1994. Notification of Acceptance September 15, 1994. Workshop November 22, 1994. Code: NLP Title: 2nd Australian Workshop on Natural Language Processing Contact: Dr. Ingrid Zukerman Email: ingrid@bruce.cs.monash.edu.au Important dates: Submission of extended abstract August 9, 1994. Notification of acceptance September 16, 1994. Full paper submission October 17, 1994. Workshop November 22, 1994. The cost of all full day workshops will be $40 with conference registration or $140 without conference registration. The Workshop on Evolutionary Computation which runs for one and a half days will cost $60 with conference registration and $160 without registration. Postgraduate Student registration will be the full registration owing less $5. Further information is available electronically or by contacting the conference secretary. POSTGRADUATE SESSION A postgraduate students session is scheduled on the 22nd November 1994. This session is specifically organised for all Post-Graduate Research students who are doing research in Artificial Intelligence. 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 the three keynote speakers. Only postgraduate students attending the conference are invited to attend this special session. If you would like to participate in this session, please email a one-page abstract which: (a) describes your research in Artificial Intelligence and (b) lists issues you would like to raise with the keynote speakers; to the following electronic email address, not later than 1st October 1994: debenham@socs.uts.edu.au TECHNICAL PROGRAMME The technical paper presentation of AI'94 will be held over the last three days (23rd - 25th November). All papers to be presented here will have been independently reviewed. The task of selecting which papers will be presented is the job of the following programme 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. David Russell; PSU, U.S.A Dr. Jennie Clothier; DSTO A/Prof. Claude Sammut; UNSW Dr. Robert Dale; Microsoft Institute A/Prof. Liz Sonenberg; Melbourne A/Prof. Wee Leng Goh; NTU, Singapore Prof. Rodney Topor; Griffith Mr. Andy Horsfall; Fujitsu Dr. Wayne Wobcke; Sydney Prof. Ray Jarvis; Monash Dr. Xindong Wu; James Cook Dr. Chris Leckie; TRL Dr. Xin Yao; ADFA Dr. Craig Lindley; CSIRO Dr. Waikiang Yeap; Otago, N.Z. ACCREDITATION The Australian Computer Society has accredited the AI'94 Programme (technical sessions) with 18 Practising Computer Professional (PCP) points. The programme of tutorial/workshop and technical sessions meet the requirements for a structured training programme as provided under the Training Guarantee (Administration) Act 1990. ORGANISING COMMITTEE Dr. Dickson Lukose (Chair) Mr. Allan Williams (Secretary) Dr. Chengqi Zhang Dr. Gregory Zevin (Treasurer) Prof. John Debenham Mr. Prakash Bhandari Ms. Gabrielle Aldridge KEYNOTE SPEAKERS In addition to the paper presentation the following international research leaders have each agreed to present a keynote address: Professor Wolfgang Wahlster, German Research Centre for AI (Microsoft Institute Sponsored keynote speaker) Date: 23rd November 1994 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 Centre 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 Modelling 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 Date: 24th November 1994 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). Professor John F. Sowa, State University of New York - Binghamton Date: 25th November 1994 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 Organisation (ISO). FURTHER INFORMATION The size of this brochure has restricted the amount of material which can be presented. For further information please use the following electronic sources of information. 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 AI'94 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 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 when necessary. 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 problems 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. If you do not have electronic access please post or fax your request for information to the Conference Secretary. ---------------------Cut Here ------------------------------------------------- REGISTRATION FORM (Please copy this form as many times as is required) Name:_____________________________________________________________________ Title Surname Given Name Preferred Name on Lapel Badge:____________________________________________ Institution/Company:_____________________________________________________ Postal Address:__________________________________________________________ __________________________________________________________ __________________________________________________________ Telephone: ___________________________ Facsimile: ___________________________ Email: ________________________________ SUMMARY OF REGISTRATION FEES Early Bird Registration Full Delegate $490.00 ____________ Early Bird Registration Student $210.00 ____________ Late Fee (must be added after 1/10/94) $100.00 ____________ Day Registration $220.00 ____________ Extra Conference Dinner Tickets ______ @$50.00 ____________ Extra Conference Proceedings ______ @$85.00 ____________ Accommodation at MWC ______ @$43.00 per night ____________ Tutorial #1 Code: __ __ __ __ ___________ Tutorial #2 Code: __ __ __ __ ___________ Workshop Code: __ __ __ __ ___________ Total in AUS $ ___________ Cheques should be marked NOT NEGOTIABLE and made payable to: AI'94 or Please charge my credit card (Please mark) Mastercard [ ] Bankcard [ ] Visa [ ] Card number ___________________________________________________________ Signature of cardholder: _____________________________________________ Expiry Date: ______________________ Special Requirements (e.g. Vegetarian Food, Disabled Access) ______________________________________________________________________ ______________________________________________________________________ To help with the booking of accommodation could you alsoplease compete the following information: I will need accommodation for the period __ November 1994 till ___ November 1994. [ ] I do not wish to book at MWC and instead wish to stay at one of the following: First Choice ____________________ Second Choice ____________________ Third Choice ____________________ If these options are unavailable please: [ ] Notify me immediately so I may select another motel [ ] Select another motel on my behalf from _____ star rating down [ ] Book me accommodation at MWC Article 23975 of comp.ai: Path: cantaloupe.srv.cs.cmu.edu!das-news.harvard.edu!news2.near.net!news.umass.edu!news.mtholyoke.edu!uhog.mit.edu!europa.eng.gtefsd.com!howland.reston.ans.net!quagga.ru.ac.za!munnari.oz.au!yarrina.connect.com.au!harbinger.cc.monash.edu.au!news.cs.su.oz.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 : CBR Keywords: Contraint-Based Reasoning Message-ID: <6627@grivel.une.edu.au> Date: 30 Aug 94 03:48:11 GMT Sender: usenet@grivel.une.edu.au Followup-To: poster Lines: 259 Nntp-Posting-Host: fermat.une.edu.au Xref: glinda.oz.cs.cmu.edu comp.ai:23975 comp.ai.genetic:3762 comp.ai.neural-nets:18617 comp.ai.nat-lang:1994 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 Prof Z. Michalewicz [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 23979 of comp.ai: Path: cantaloupe.srv.cs.cmu.edu!das-news.harvard.edu!news2.near.net!news.umass.edu!news.mtholyoke.edu!uhog.mit.edu!europa.eng.gtefsd.com!howland.reston.ans.net!agate!msuinfo!harbinger.cc.monash.edu.au!news.cs.su.oz.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 : EC Keywords: Evolutionary Computation Message-ID: <6628@grivel.une.edu.au> Date: 30 Aug 94 03:49:34 GMT Sender: usenet@grivel.une.edu.au Followup-To: poster Lines: 258 Nntp-Posting-Host: fermat.une.edu.au Xref: glinda.oz.cs.cmu.edu comp.ai:23979 comp.ai.genetic:3766 comp.ai.neural-nets:18621 comp.ai.nat-lang:1998 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 Prof Z. Michalewicz Department of Computer Science University of North Carolina --- Charlotte Charlotte, NC 28223, U.S.A. Email: zbyszek@mosaic.uncc.edu Phone: (704)547-4873 Fax: (704)547-3516 and 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 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 cover both evolutionary optimisation and evolutionary learning. 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: Session~1 --------- 1. Introduction (a) A Definition of Evolutionary Computation (b) Major Branches of Evolutionary Computation 2. Genetic Algorithms (a) A Simple Genetic Algorithm (b) Selection (c) Crossover (d) Mutation (e) Other Genetic Operators Session~2 --------- 1. Evolution Strategies and Evolutionary Programming (a) A Brief History (b) Adaptive Mutation 2. Hybrid Algorithms 3. Evolutionary Learning (a) Classifier Systems (b) Evolutionary Artificial Neural Networks (c) Artificial Life Session~3 --------- Practical experiments. BIO-DATA OF THE PRESENTERS Dr Zbigniew Michalewicz is a professor at the Department of Computer Science, University of North Carolina---Charlotte, Charlotte, NC 28223, USA. He is one of the leading researchers in the field of evolutionary computation. He has published a monograph and numerous technical papers in international journals and conference proceedings. He is the general chairman of the First IEEE International Conference on Evolutionary Computation held in Orlando, 27--29 June 1994. He is also in the program committee of many other international conferences. Professor Michalewicz is a member of the editorial board of the journal "Evolutionary Computation" and "Statistics and Computing". He is a member of the ACM. Dr Xin Yao is 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, and evolutionary and hybrid learning systems. He has published more than 20 technical papers in international journals and conference proceedings. He co-organised AI'93 and AI'94 Workshops on Evolutionary Computation and is in the Program Committee of ICYCS'93, IEEE EC'95, IEA-AIE'95. He is a member of IEEE and IEEE Computer Society. PREREQUISITES 1. Rudimentary knowledge of probability. 2. 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 Prof Z. Michalewicz [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 23976 of comp.ai: Path: cantaloupe.srv.cs.cmu.edu!das-news.harvard.edu!news2.near.net!noc.near.net!howland.reston.ans.net!agate!msuinfo!harbinger.cc.monash.edu.au!news.cs.su.oz.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 : KAM Keywords: Knowledge Acquisition Message-ID: <6629@grivel.une.edu.au> Date: 30 Aug 94 03:50:47 GMT Sender: usenet@grivel.une.edu.au Followup-To: poster Lines: 316 Nntp-Posting-Host: fermat.une.edu.au Xref: glinda.oz.cs.cmu.edu comp.ai:23976 comp.ai.genetic:3763 comp.ai.neural-nets:18618 comp.ai.nat-lang:1995 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 Prof Z. Michalewicz [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 23977 of comp.ai: Path: cantaloupe.srv.cs.cmu.edu!das-news.harvard.edu!news2.near.net!noc.near.net!howland.reston.ans.net!agate!msuinfo!harbinger.cc.monash.edu.au!news.cs.su.oz.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 : FLFC Keywords: Fuzzy Logic Message-ID: <6630@grivel.une.edu.au> Date: 30 Aug 94 03:52:00 GMT Sender: usenet@grivel.une.edu.au Followup-To: poster Lines: 251 Nntp-Posting-Host: fermat.une.edu.au Xref: glinda.oz.cs.cmu.edu comp.ai:23977 comp.ai.genetic:3764 comp.ai.neural-nets:18619 comp.ai.nat-lang:1996 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 Prof Z. Michalewicz [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 23978 of comp.ai: Path: cantaloupe.srv.cs.cmu.edu!das-news.harvard.edu!news2.near.net!noc.near.net!howland.reston.ans.net!agate!msuinfo!harbinger.cc.monash.edu.au!news.cs.su.oz.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 : HS Keywords: Hybrid Systems Message-ID: <6631@grivel.une.edu.au> Date: 30 Aug 94 03:53:05 GMT Sender: usenet@grivel.une.edu.au Followup-To: poster Lines: 270 Nntp-Posting-Host: fermat.une.edu.au Xref: glinda.oz.cs.cmu.edu comp.ai:23978 comp.ai.genetic:3765 comp.ai.neural-nets:18620 comp.ai.nat-lang:1997 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 Prof Z. Michalewicz [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 23980 of comp.ai: Path: cantaloupe.srv.cs.cmu.edu!das-news.harvard.edu!news2.near.net!news.umass.edu!news.mtholyoke.edu!uhog.mit.edu!europa.eng.gtefsd.com!howland.reston.ans.net!agate!msuinfo!harbinger.cc.monash.edu.au!news.cs.su.oz.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 : IDSS Keywords: Intelligent Decision Support Systems Message-ID: <6632@grivel.une.edu.au> Date: 30 Aug 94 03:54:14 GMT Sender: usenet@grivel.une.edu.au Followup-To: poster Lines: 280 Nntp-Posting-Host: fermat.une.edu.au Xref: glinda.oz.cs.cmu.edu comp.ai:23980 comp.ai.genetic:3767 comp.ai.neural-nets:18622 comp.ai.nat-lang:1999 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 Prof Z. Michalewicz [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 23981 of comp.ai: Path: cantaloupe.srv.cs.cmu.edu!das-news.harvard.edu!news2.near.net!news.umass.edu!news.mtholyoke.edu!uhog.mit.edu!europa.eng.gtefsd.com!howland.reston.ans.net!agate!msuinfo!harbinger.cc.monash.edu.au!news.cs.su.oz.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 : ILDB Keywords: Intelligent Learning database Systems Message-ID: <6633@grivel.une.edu.au> Date: 30 Aug 94 03:55:22 GMT Sender: usenet@grivel.une.edu.au Followup-To: poster Lines: 255 Nntp-Posting-Host: fermat.une.edu.au Xref: glinda.oz.cs.cmu.edu comp.ai:23981 comp.ai.genetic:3768 comp.ai.neural-nets:18623 comp.ai.nat-lang:2000 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 Prof Z. Michalewicz [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 23982 of comp.ai: Path: cantaloupe.srv.cs.cmu.edu!das-news.harvard.edu!news2.near.net!news.umass.edu!news.mtholyoke.edu!uhog.mit.edu!europa.eng.gtefsd.com!howland.reston.ans.net!agate!msuinfo!harbinger.cc.monash.edu.au!news.cs.su.oz.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 : NMR (Note: Student Scholarships) Keywords: Nonmonotonic Reasoning Message-ID: <6634@grivel.une.edu.au> Date: 30 Aug 94 04:00:05 GMT Sender: usenet@grivel.une.edu.au Followup-To: poster Lines: 289 Nntp-Posting-Host: fermat.une.edu.au Xref: glinda.oz.cs.cmu.edu comp.ai:23982 comp.ai.genetic:3769 comp.ai.neural-nets:18624 comp.ai.nat-lang:2001 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. SCHOLARSHIPS (a) Two scholarships will be granted for the full cost of the tutorial. To be considered for these scholarships submit the following information to Dr Mary-Anne Williams by October 7, 1994: (i) Name, contact address, email (if available), (ii) Academic Record, (iii) Statement of research interests, (iv) Recommendations from two academics acquainted with the candidates record. (b) Criteria to be satisfied by the applicants: Must have, or anticipate receiving a good Honours Degree by the end of 1994. (c) Procedure for selection of the candidates that will receive the scholarships: Successful candidates will be notified of their success or otherwise by October 14, 1994. 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 Prof Z. Michalewicz [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 23983 of comp.ai: Path: cantaloupe.srv.cs.cmu.edu!das-news.harvard.edu!news2.near.net!news.umass.edu!news.mtholyoke.edu!uhog.mit.edu!news.kei.com!yeshua.marcam.com!usc!howland.reston.ans.net!agate!msuinfo!harbinger.cc.monash.edu.au!news.cs.su.oz.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 : TFML Keywords: Theoretical Foundations of Machine Learning Message-ID: <6635@grivel.une.edu.au> Date: 30 Aug 94 04:01:15 GMT Sender: usenet@grivel.une.edu.au Followup-To: poster Lines: 276 Nntp-Posting-Host: fermat.une.edu.au Xref: glinda.oz.cs.cmu.edu comp.ai:23983 comp.ai.genetic:3770 comp.ai.neural-nets:18625 comp.ai.nat-lang:2002 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 Prof Z. Michalewicz [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 23984 of comp.ai: Path: cantaloupe.srv.cs.cmu.edu!das-news.harvard.edu!news2.near.net!news.umass.edu!news.mtholyoke.edu!uhog.mit.edu!europa.eng.gtefsd.com!library.ucla.edu!ihnp4.ucsd.edu!munnari.oz.au!yarrina.connect.com.au!harbinger.cc.monash.edu.au!news.cs.su.oz.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 : PROVISIONAL PROGRAMME Keywords: Seventh Australian Joint Conference on Artificial Intelligence (AI'94) Message-ID: <6637@grivel.une.edu.au> Date: 30 Aug 94 04:15:16 GMT Sender: usenet@grivel.une.edu.au Followup-To: poster Lines: 676 Nntp-Posting-Host: fermat.une.edu.au Xref: glinda.oz.cs.cmu.edu comp.ai:23984 comp.ai.genetic:3771 comp.ai.neural-nets:18626 comp.ai.nat-lang:2003 PROVISIONAL PROGRAMME OF AI'94 Seventh Australian Joint Conference on Artificial Intelligence ======================================================================= 21 - 25, November, 1994 The University of New England, Armidale, NSW, Australia For Technical Sessions Contact: Chengqi Zhang Programme Committee Co-Chair Email: ai94@fermat.une.edu.au Tel: (61-67) 73 2350 (or message 61-67 73 2298) Fax: (61-67) 73 3312 For Workshops or Tutorials Contact: Dickson Lukose Organising Committee Chair Email: ai94@fermat.une.edu.au Tel: (61-67) 73 2302 (or message 61-67 73 2298) Fax: (61-67) 73 3312 ============================================================================= OUTLINES of PROGRAMME MONDAY, 21 November 1994 (TUTORIALS) -------------------------------------------------------------------------- Stream1 Stream2 Stream3 Stream4 WORKSHOP -------------------------------------------------------------------------- 8.30- 9.30 REGISTRATION for TUTORIALS and WORKSHOPS --------------------------------------------------------------------------- 9.30-10.30 Introduction to Artificial Intelligence --------------------------------------------------------------------------- 10.30-11.00 Morning Tea Break --------------------------------------------------------------------------- 11.00-12.30 NMR(t) IDSS(t) EC(t) TFML(t) --------------------------------------------------------------------------- 12.30- 2.00 LUNCH BREAK -------------------------------------------------------------------------- 2.00- 3.30 NMR(t) IDSS(t) EC(t) TFML(t) ECW -------------------------------------------------------------------------- 3.30- 4.00 Afternoon Tea Break -------------------------------------------------------------------------- 4.00- 5.30 NMR(p) IDSS(p) EC(p) TFML(t) ECW ========================================================================== TUESDAY, 22 November 1994 TUTORIALS -------------------------------------------------------------------------- Stream1 Stream2 Stream3 Stream4 Stream5 -------------------------------------------------------------------------- 9.00-10.30 CBR(t) FLFC(t) HS(t) ILDB(t) KAM(t) --------------------------------------------------------------------------- 10.30-11.00 Morning Tea Break --------------------------------------------------------------------------- 11.00-12.30 CBR(t) FLFC(t) HS(t) ILDB(t) KAM(t) --------------------------------------------------------------------------- 12.30- 2.00 LUNCH BREAK -------------------------------------------------------------------------- 2.00- 3.30 CBR(t) FLFC(p) HS(d) ILDB(p) KAM(d) -------------------------------------------------------------------------- 3.30- 4.00 Afternoon Tea Break -------------------------------------------------------------------------- 4.00- 5.30 CBR(p) -------------------------------------------------------------------------- WORKSHOPS -------------------------------------------------------------------------- Stream1 Stream2 Stream3 Stream4 Stream5 -------------------------------------------------------------------------- 9.00-10.30 CS ES KBNR NLP ECW --------------------------------------------------------------------------- 10.30-11.00 Morning Tea Break --------------------------------------------------------------------------- 11.00-12.30 CS ES KBNR NLP ECW --------------------------------------------------------------------------- 12.30- 2.00 LUNCH BREAK -------------------------------------------------------------------------- 2.00- 3.30 CS ES KBNR NLP ECW -------------------------------------------------------------------------- 3.30- 4.00 Afternoon Tea Break POSTGRADUATE ----------------------------------------------------------- STUDENTS 4.00- 5.30 CS ES KBNR NLP ECW SESSION -------------------------------------------------------------------------- 6.00- 7.30 WELCOME B.B.Q. ========================================================================== WEDNESDAY, 23 November 1994 (TECHNICAL SESSIONS) -------------------------------------------------------------------------- Stream1 Stream2 Stream3 -------------------------------------------------------------------------- 8.00- 9.00 REGISTRATION for CONFERENCE -------------------------------------------------------------------------- 9.00- 9.30 OPENING and WELCOME ------------------------------------------------------------------------- 9.30-10.00 "Intellimedia: Planning Language, Graphics and INVITED SPEECH Layout for Adaptive Information Presentation" 10.00-10.30 Prof. W. Wahlster (DFKI) --------------------------------------------------------------------------- 10.30-11.00 Morning Tea Break --------------------------------------------------------------------------- 11.00-12.30 NL(1) ML(1) KR(1) --------------------------------------------------------------------------- 12.30- 2.00 LUNCH BREAK -------------------------------------------------------------------------- 2.00- 3.30 RE(1) KBS&AIA(1) NN(1) -------------------------------------------------------------------------- 3.30- 4.00 Afternoon Tea Break -------------------------------------------------------------------------- 4.00- 5.30 RE(2) ML(2) KR(2) ========================================================================== THURSDAY, 24 November 1994 (TECHNICAL SESSIONS) -------------------------------------------------------------------------- Stream1 Stream2 Stream3 -------------------------------------------------------------------------- 9.00- 9.30 "The Present and Future of Distributed INVITED SPEECH Artificial Intelligence" 9.30-10.00 Prof. K. Sycara (CMU) -------------------------------------------------------------------------- 10.00-10.30 Morning Tea Break --------------------------------------------------------------------------- 10.30-12.30 DAI(1) KBS&AIA(2) PLANNING --------------------------------------------------------------------------- 12.30- 2.00 LUNCH BREAK -------------------------------------------------------------------------- 2.00- 3.30 NL(2) ML(3) Robotics -------------------------------------------------------------------------- 3.30- 4.00 Afternoon Tea Break -------------------------------------------------------------------------- 4.00- 5.00 RE(3) ML(4) NN(2) -------------------------------------------------------------------------- 5.00- 6.30 POSTER SESSION -------------------------------------------------------------------------- 7.00- *** CONFERENCE DINNER ========================================================================= FRIDAY, 25 November 1994 (TECHNICAL SESSIONS) -------------------------------------------------------------------------- Stream1 Stream2 Stream3 -------------------------------------------------------------------------- 9.00- 9.30 INVITED SPEECH "Sharing and Integrating Knowledge Bases" 9.30-10.00 Prof. J. F. Sowa (State Univ. of NY) -------------------------------------------------------------------------- 10.00-10.30 Morning Tea Break --------------------------------------------------------------------------- 10.30-12.30 RE(4) ML(5) IA&Vision --------------------------------------------------------------------------- 12.30- 2.00 LUNCH BREAK -------------------------------------------------------------------------- 2.00- 3.30 DAI(2) KA KR(3) -------------------------------------------------------------------------- 3.30- 4.00 CLOSING ========================================================================= THE DETAILED PROGRAMME Monday, 21 November, 1994 REGISTRATION for TUTORIALS, WORKSHOPS 8.30am - 9.30am NB: Morning Tea: 10.30am - 11.00am Lunch: 12.30pm - 2.00pm Afternoon Tea: 3.30pm - 4.00pm TUTORIALS (First Day) ________________________________________________________________________ Code Length Title Presenter(s) time ------------------------------------------------------------------------ AI 1hr Introduction to Artificial Prof. James Alty 9.30-10.30 Intelligence NMR 3s Nonmonotonic Reasoning Dr. M-A Williams 11.00-5.30 Dr. G. Antoniou IDSS 3s Building Intelligent Dr. J. Zeleznikow 11.00-5.30 Decision Support Mr. Dan Hunter Systems EC 3s An Introduction to Dr. X. Yao 11.00-5.30 Evolutionary Computation Prof. Z. Michalewicz TFML 3s Theoretical Foundations of Dr. A. G. Hoffman 11.00-5.30 Machine Learning Dr. Shyam Kapur Dr. Arun Sharma ------------------------------------------------------------------------- WORKSHOPS (First Day) ________________________________________________________________________ Code Length Title Contact time ------------------------------------------------------------------------ ECW 2s AI'94 Workshop on Dr. X. Yao 2.00-5.30 Evolutionary Computation ========================================================================== Tuesday, 22nd November NB: Morning Tea: 10.30am - 11.00am Lunch: 12.30pm - 2.00pm Afternoon Tea: 3.30pm - 4.00pm TUTORIALS (Second Day) ___________________________________________________________________________ Code Length Title Presenter(s) Time --------------------------------------------------------------------------- CBR 4s Constraint-Based Reasoning Dr. H. W. Guesgen 9.00-5.30 ILDB 3s Intelligent Learning Dr Xindong Wu 9.00-3.30 Database Systems FLFC 3s Fundamentals of Fuzzy Dr. A. Sekercioglu 9.00-3.30 Logic and Fuzzy Logic Mr. G. K. Egan Controllers KAM 3s Knowledge Acquisition Dr. P. Compton 9.00-3.30 and Maintenance with Ripple Down Rules HS 3s Hybrid (AI symbolic, Dr. Nik K. Kasabov 9.00-3.30 Connectionist, Fuzzy, Chaotic) Systems --------------------------------------------------------------------------- WORKSHOPS (Second Day) ________________________________________________________________________ Code Length Title Contact time ------------------------------------------------------------------------ CS 4s 1st Australian Conceptual Mr. Gerard Ellis 9.00-5.30 Structures Workshop ES 4s AI'94 Workshop on Expert Dr. Eric Tsui 9.00-5.30 Systems in Production use KBNR 4s AI'94 Workshop on Dr. John Weckert 9.00-5.30 Knowledge-Based Systems in Natural Resource Management NLP 4s 2nd Australian Workshop on Dr. Ingrid Zukerman 9.00-5.30 Natural Language Processing ECW 4s AI'94 Workshop on Dr. X. Yao 9.00-5.30 Evolutionary Computation ----------------------------------------------------------------------------- POSTGRADUATE STUDENTS SESSION 4.00pm - 5.30pm ----------------------------------------------------------------------------- WELCOME B.B.Q. 6:00pm - 7:30pm REGISTRATION for CONFERENCE 4.00pm - 8.30pm ============================================================================ Wednesday, 23 November, 1994 REGISTRATION for CONFERENCE 8.00am - 9.00am MORNING SESSION OPENING and WELCOME 9:00am - 9:30am INVITED SPEECH 9:30am - 10:30am "Intellimedia: Planning Language, Graphics and Layout for Adaptive Information Presentation" Prof. W. Wahlster (DFKI) (Microsoft Institute Sponsored) TEA BREAK 10:30am - 11:00am 11:00am - 12:30pm STREAM 1: Natural Language NL(1) Improving Speech Understanding through Integration of Prosody and Syntax A. J. Hunt An Automated Grouping of Segments In a Line-Chart to Explain its Movement H. Nakajima and M. Oku Analysing relative clauses of a semi-free word order language C. R. Shankar STREAM 2: Machine Learning ML(1) Past Tense of Verbs and First-Order Learning J. R. Quinlan Learning Structural Concept from 3-D Geometric Model of Objects G. Dong, M. Yachida and T. Yamaguchi Empirical Function Attribute Construction in Classification Learning S. P. Yip and G. I. Webb STREAM 3: Knowledge Representation KR(1) Entrenchment Kinematics 101 A. C. Nayak, N. Y. Foo, M. Pagnucco and A. Sattar Algorithms for Iterative Belief Revision W. Wobcke Constraints and Updates N. Foo and Y. Zhang LUNCH 12:30pm - 2:00pm AFTERNOON SESSION 2.00pm - 3.30pm STREAM 1: Reasoning RE(1) A Logical Foundation of Evidences T. G. Tang and N. Foo Termination of Combination of Composable Term Rewriting Systems M. Kurihara and A. Ohuchi Efficient Algorithms for Automated Reasoning in Knowledge Based Systems J. W. Guan and D. A. Bell STREAM 2: Knowledge Based Systems and AI Applications KBS&AIA(1) A Unified Model for Rule-Based Knowledge Systems J. K. Debenham An Indexing Framework for Adaptive Problem Sequencing and Problem Simplification T. Hirashima, A. Kashihara and J. Toyoda Four Issues that Distinguish a Rule-Based Control Shell from an Expert System P. Piggott and P. McKerrow STREAM 3: Neural Networks NN(1) A General Design for Temporal Sequence Processing Using Any Arbitrary Associative Neural Network L. Wang A Percptron Adaptation Algorithm for Neural Networks and Its Application X. H. Yu Perception of Object Characteristics By The Interpretation of Ultrasonic Range Data N. L. Harper and P. J. McKerrow TEA BREAK 3:30pm - 4:00pm 4.00pm - 5.30pm STREAM 1: Reasoning RE(2) On Reflexive Assumption-based Framework for Autoepistemic Logic Y. Aramkulchai and Y. J. Jiang Default Reasoning as Partial Constraint Satisfaction A. K. Ghose, A. Sattar and R. Goebel Adding Disjunction to Defeasible Logic D. Billington STREAM 2: Machine Learning ML(2) Intrinsic Classification by MML - the Snob Program C. S. Wallace and D. L. Dowe An Inductive Principle for Learning Logical Definitions from Relations N. Y. Foo and T. G. Tang A General Framework for Concept Formation G. Gibbon and N. Foo STREAM 3: Knowledge Representation KR(2) Primitive Events N. Foo and P. Peppas On Combinatorial Possible Worlds C. Nowak and P. W. Eklund Problem Map: A Hybrid Knowledge Representation Scheme D. Lukose _______________________________________________________________________ THURSDAY, 24 November, 1994 MORNING SESSION INVITED SPEECH 9:00am - 10:00am "The Present and Future of Distributed Artificial Intelligence" Prof. K. Sycara (CMU) TEA BREAK 10:00am - 10:30am 10:30am - 12:30pm STREAM 1: Distributed Artificial Intelligence DAI(1) Plan Reuse in Cooperative Distributed Problem Solving T. Sugawara A Multi-agent Architecture for Multimedia Presentation Planning Y. Han and I. Zukerman Synthesis of Solutions in Distributed Expert Systems M. Zhang and C. Zhang Fundamental Approach to Dynamic Subgoal Regeneration of Autonomous Agents Moving in A Lattice World T. Watanabe, T. Yamazaki and T. Mizuno STREAM 2: Knowledge Based Systems and AI Applications KBS&AIA(2) Documentation of Design and Structure in Expert Systems Rule Base P. Crowther, J. Hartnett and R. Williams Chaining in Rule-Based Systems G. Fang and X. Wu Quantitative Evaluation of Classification Performance Using Roc Analysis for a Neural Net Comparison with a Nearest Neighbour Algorithm P. A. Guignard and C. Chung Efficient Probabilistic Inference through Index Expression Belief Networks P. D. Bruza and J. J. Ijdens STREAM 3: Planning and Scheduling Towards the Deductive Synthesis of Nonlinear Plans S. J. S. Cranefield An Information-theoretic Approach to Decision-making during Consultations B. Raskutti and I. Zukerman On Load Balancing with Hyperplanes and Chromosomes F. Kirschnick Using the BOXES Methodology as a Possible Stabilizer of Lorentz Chaos D. Russell LUNCH 12:30 - 2:00 AFTERNOON SESSION 2:00 - 3:30 STREAM 1: Natural Languages NL(2) Concept Learning from Japanese Copular Sentences Using Heuristics K. Araki and Y. Momouchi A Probabilistic Model of Compound Nouns M. Lauer and M. Dras A Relevance-Based Approach to Interpreting Contextualized Metaphors A. Utsumi and M. Sugeno STREAM 2: Machine Learning ML(3) Generality is more significant than complexity: Toward an alternative to Occam's Razor G. I. Webb Applying Inductive Logic Programming to Causal Qualitative Models in Neuroendocrinology A. Mahidadia, P. Compton and C. Sammut Learning Triangulated Causal Networks from Data B. Stewart STREAM 3: Robotics Real-World Path Following for a Monocular Vision based Autonomous Mobile Robot in a 'Remote-Brain' Environment A. Lipton and S. Kagami Motion Planning in an L-shaped Corridor N. Ahmed and A. Sowmya Short-Lived Navigational Markers and Scheduling Patrolling Robots R. A. Russell and D. Thiel TEA BREAK 3:30pm - 4:00pm 4:00pm - 5:00pm STREAM 1: Reasoning RE(3) Abductive Expansion: Abductive Inference and the Process of Belief Change M. Pagnucco, A. Nayak and N. Foo Lemmas and Links in Analytic Tableau J. M. Coldwell and G. Wrightson STREAM 2: Machine Learning ML(4) Dynamic Memory Approaches to Qualitative Prediction W. Wang and J. S. Gero The Application of PbD Methods to Real World Domains - Two Case Studies S. Munch, M. Sassin and S. Bocionek STREAM 3: Neural Networks NN(2) Concept Learning from Japanese Copular Sentences Using Heuristics K. Araki and Y. Momouchi A Probabilistic Model of Compound Nouns M. Lauer and M. Dras A Relevance-Based Approach to Interpreting Contextualized Metaphors A. Utsumi and M. Sugeno 5.00pm - 6.00pm Poster Session * CONFERENCE DINNER 7:00pm - * _______________________________________________________________________ FRIDAY, 19 November, 1993 MORNING SESSION INVITED SPEECH 9:00am - 10:00am "Sharing and Integrating Knowledge Bases" Prof. J. F. Sowa (State Univ. of NY) TEA BREAK 10:00am - 10:30am 10:30am - 12:30am STREAM 1: Reasoning RE(4) Some Proof Procedures and Their Application for Realizing Another Fuzzy Prolog H. Sakai Qualitative Models for Large Scale Physical Systems R. Hewett Integrating Qualitative Reasoning into Complex Quantitative Analysis: Introduction to a Real System Q. Zhao and T. Nishida Convergence and the Classifier System S. Hedges and A. Hunter STREAM 2: Machine Learning ML(5) Learning of Inexact Rules By the FISH-NET Algorithm From Low Quality Data H. Dai and V. Ciesielski Characteristics of Data Suitable for Learning with Connectionist and Symbolic Methods P. A. Collier and S. G. Waugh Genetic Programming and Spatial Information P. A. Whigham STREAM 3: Image Analysis and Vision Image interpretation systems with context J. Aisbett and G. Gibbon Scale and Rotation Invariant Texture Analysis Based on Structural Property R. K. Goyal, W. L. Goh, P. D. Mital and K. L. Chan Can the sun's direction be estimated prior to the determination of shape? W. Chojnacki, M. J. Brooks and D. Gibbins Model for Recognition and Correspondence Matching of Objects B. Bhavnagri LUNCH 12:30pm - 2:00pm AFTERNOON SESSION 2:00pm - 3:30pm STREAM 1: Distributed Artificial Intelligence DAI(2) An Adaptable and Dynamic Architecture for Distributed Problem Solving Based on the Blackboard Paradigm J. Neves, M. Santos and V. Alves DISA: Distributed Resource Management Based on Problem Decomposition & Temporal Abstractions P. M. Berry, B. Y. Choueiry and L. Friha Using Agents to Represent Organisational Context J. O'Neill and J. Clothier STREAM 2: Knowledge Acquisition Extracting Rule Schemas from Rules, for an Intelligent Learning Database System G. Sutcliffe and X. Wu Knowledge Acquisition by Use of Statistics C. Seibold A precise Semantics for Vague Diagrams T. Menzies and P. Compton STREAM 3: Knowledge Representation KR (3) Toulmin Argument Structures as a Higher Level Abstraction for Hybrid Reasoning A. Stranieri, M. Gawler and J. Zeleznikow A Visual Representation of Grounded Theory Construction H. K. Yuen and T. J. Richards Closing 3:30pm - 4:00pm _____________________________________________________________________ ACKNOWLEDGEMENTS We gratefully acknowledge the support of the following organisations: 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, Department of Mathematics, Statistics, and Computing Science (UNE), and Key Centre for Advanced Computing Sciences, University of Technology, Sydney. ___________________________________________________________________________