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Computer Science Department
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213
(412)268-2565
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Spring 1998 Ph.D. Course Descriptions Computer Science Department Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213 (412)268-2565 |
15-750(A) Algorithms Core
Gary Miller
MW 10:30 - 11:50, WeH 5409 A&B
Units: 12
Coreunits: 1
NO DESCRIPTION AVAILABLE AT THIS TIME
15-780(A) ARTIFICIAL INTELLIGENCE CORE
Mitchell/Veloso
TR 10:30-11:50, WeH 5409 A&B
1 Coreunit, 12 University Units
Permission of instructor required for students not enrolled in CS PHD
program
DESCRIPTION: This is the graduate core level course on artificial intelligence: The course will focus on the ai algorithms and techniques to build a full intelligent agent with cognition, action, and perception. Particular focus will be given on the cognition aspects, which enable the agent to represent the world, to do effective problem solving, and to improve its world modelling and problem solving performance through machine learning. Agents will act in uncertain and dynamic environments.
15-810(A) Verification of Concurrent, Reactive, & Real-Time Prgrms
Clarke
Tuesday, 3:00-4:30, WeH 4601
1/2 Coreunit (Approved), 6 University Units
This course is a graduate level research seminar on automatic verification techniques for concurrent, reactive, and real-time programs. It was last taught in the fall of 1996, but different topics will be covered this semester.
DETAILED COURSE DESCRIPTION: Logical errors in hardware controllers, communication protocols, and real-time programs are becoming an increasingly important problem. They can delay getting a new product on the market or cause the failure of some critical device that is already in use. The most widely used method for verifying such systems is based on extensive simulation and can easily miss significant errors when the program is very complicated.
Many of these programs can be viewed as having only a finite number of states. When this is the case, an alternative verification technique called "model checking" may be used. In this approach specifications are expressed by automata or temporal logic formulas, and programs are modeled as state transition systems. An efficient search procedure is used to determine automatically if the specifications are satisfied by the transition system. The technique has been used in the past to find subtle errors in a number of non-trivial designs. Recently, the size of the state transition systems that can be verified by model checking methods has increased dramatically. By representing transition relations implicitly using Binary Decision Diagrams (BDDs), it has become possible to check some examples with more than 10^100 states!
PREREQUISITIES: The prerequisites for the course are minimal--basic knowledge of elementary logic and automata theory.
EVALUATION: Students taking the class for graduate credit will be asked to prepare a short project and give one or two lectures. Auditors are also welcomed.
POSSIBLE TOPICS TO BE COVERED:
15-812(A) Semantics of Programming Languages
Brookes
MW 1:30-2:50, WeH 4601
1 Coreunit (Pending DRC approval), 12 University Units
DESCRIPTION: This course will present a selection of major topics in the semantics of programming languages. The emphasis will be on the mathematical formulation of fundamental concepts, the use of denotational and operational semantics to support program analysis and synthesis, and the practical roles of formal semantics in supporting programming methodology and the development of tools for reasoning about program behavior. In each section of the course we will demonstrate the utility of semantics in analysing the behavior of some non-trivial programming examples. We will also focus on general laws of program equivalence. We will introduce the relevant mathematical, logical, and categorical concepts as the need arises. Students will have the opportunity to write programs and semantic definitions in Standard ML, to permit easy prototyping and debugging.
The topics to be covered will include:
PRE-REQUISITES: There will not be a required text. Notes and papers will be distributed. CS students should have already taken the PL core.
METHOD OF EVALUATION: (for CS graduate students). For students intending to take the course for core credit (1 unit), grading will be based on performance in a series of homeworks, involving a combination of written and programming assignments, a midterm exam, and a final exam.
NOTE: This course should be accessible to graduate students in CS, Mathematics, and Philosophy, including Pure and Applied Logic students as well as regular CS students. For CS students having taken the PL core this course should provide greater depth and breadth and will serve as a solid foundation for those intending to do research in the Programming Languages area.
15-816(B) Linear Logic
INSTRUCTOR: Frank Pfenning
SCHEDULE: TR 1:30-2:50, WeH 4601
1 Coreunit (approved), 12 University Units
DESCRIPTION: The course provides an introduction to linear logic with an emphasis on its applications in computer science. This includes the theory of functional, logic and imperative programming languages. We will also develop a linear type theory which will serve as a meta-language in which the theory of programming languages with state can be formalized effectively. We hope that an implementation of the type theory (currently in progress) will be available for practical experiments.
PREREQUISITES: General familiarity with functional programming would be helpful. Interested undergraduates are welcome, but require permission by the instructor.
TEXT: Course notes will be handed out.
METHOD OF EVALUATION: Course grade will be based on a combination of weekly homework and a term project or final take-home exam. The term project may or may not involve a significant programming component, but must include a term paper.
TOPICS TO BE COVERED:
REMARK: An earlier version of a course with this title was organized as a 1/2 CU seminar. This is a 1 CU course. Course Home Page
15-819(A)Categorical Logic
Cross-listed with 80-415/715
Awodey (Philosophy)
TR 12:00-1:20
, HBH 1511
1 Coreunit Approved, 12 University Units
DESCRIPTION: Category theory has wide-ranging applications in mathematics and cognate disciplines like logic and computer science. This course focuses on the latter types of applications, a field known generally as categorical logic. A leading idea is functorial semantics - first enunciated by Lawvere in 1960. According to this, a model of a logical theory is a set-valued functor on a category determined by the theory. This simple and beautiful idea gives rise to a syntax-invariant notion of a theory and introduces many algebraic methods into logic, leading naturally to the universal and other general models that distinguish functorial from classical semantics. Such categorical models often occur in computer science, for example in denotational semantics. Another logical topic occurring in computer science is the lambda-calculus, and it is perhaps best treated via the theory of cartesian closed categories. Similarly, the notion of higher-order logic receives elegant categorical expression in the theory of topoi. Many other logical topics such as computability theory also have category-theoretical treatments, which invariably shed light on their relations to other fields, both in logic and beyond
PREREQUISITES: The course presumes familiarity with basic category theory. We begin, however, with a review of the necessary prerequisites, so that a determined but uninitiated student could in principle acquire the needed background.
TEXT: Required: McLarty, Colin: Elementary categories, elementary toposes. Oxford Logic Guides 21, Oxford University Press, 1992.
Recommended: Barr, M. & Wells, C.: Category Theory for Computing Science. Prentice Hall International Series in Computer Science, Prentice Hall, 1990.
METHOD OF EVALUATION: Grading will be based on regular homework and a final exam. Graduate students will in addition prepare and lecture on special topics to be provided by the instructor.
TOPICS TO BE COVERED:
15-822(A) System Design & Implementation Practicum
Satya
T 3:00-6:00, WeH 4615A
1 Coreunit (Pending DRC approval), 12 University Units
Enrollment Limit: 12, permission of instructor only
NO ON-LINE REGISTRATION;
Interested parties should send e-mail to satya@cs indicating name, year of
studies, home department, and giving a short summary (1-2 paragraphs) of
his/her background in the systems area. Once approval is received from the
professor, see Sharon Burks in WeH 4216 to register.
15-824(A) Mobile and Wireless Networking
Johnson
TR, 10:30-11:50, WeH 4615A
1 Coreunit (approved), 12 University Units
DESCRIPTION: Mobile computing devices such as laptop and palmtop computers are becoming widely available at very affordable prices, and many new wireless networking products and services are becoming available based on technologies such as spread-spectrum radio, infrared, cellular, and satellite. Mobile computers today often are as capable as many home or office desktop computers and workstations, featuring powerful CPUs, large main memories, hundreds of megabytes of disk space, multimedia sound capabilities, and color displays. Reasonably high-speed local area wireless networks are commonly available with speeds up to 2 megabits per second, and wide-area wireless networks are available that provide metropolitan or even nationwide service.
However, wireless networks have fundamentally different properties than typical wired networks, including higher error rates, lower bandwidths, nonuniform transmission characteristics, increased usage costs, increased susceptibility to interference and eavesdropping, and higher variability of performance. Similarly, mobile nodes behave differently and have fundamentally different limitations than stationary nodes. For example, mobile nodes generally operate on limited battery power and may move and change their point of connection to the network.
This course will examine the emerging area of mobile and wireless communications, through readings, lectures, class discussions, and a course project. We will address the topic more from the point of view of a computer scientist than a radio or cellular systems engineer, but will also cover the relevant aspects of the physical media. We will concentrate on the network protocols and other systems aspects of mobile and wireless communications. Each student will be expected to complete a course project, including a project proposal, research, and a final report. The project will be done in small groups of students, and each group will be able to choose their own topic for the project with approval of the instructor.
PREREQUISITES: Students should have had at least an undergraduate operating systems course and should have some familiarity with computer networking and networking protocols.
TEXT: The course will be based on reading assignments from recent journal and conference proceedings.
METHOD OF EVALUATION: Grades will be based on a midterm exam, a final exam, the course project, and class participation.
TOPICS TO BE COVERED: Topics that will be covered in the class include:
15-540/15-825 Rapid Prototyping of Computer Systems
Also (X-listed as 39-648 and 18-745)
Siewiorek
MW 2:30-3:50
HbH 2224
1 Coreunit (Pending DRC approval), 12 University Units
BRIEF DESCRIPTION. This is a project oriented course which will deal with all four aspects of project development: the application, the artifact, the computer-aided design environment, and physical prototyping. The class, in conjunction with the instructors, will develop specifications for a computer system to assist in bridge inspection and maintenance with a local company. The application will be partitioned between human computer interaction, electronics, industrial design, mechanical, and software components. The class will be divided into groups to specify, design, and implement the various subsystems. The goal is to produce a working hardware/software prototype of the system and to evaluate the user acceptability of the system. We will also monitor our progress in the design process by capturing our design escapes (error) with Orthogonal Defect Classification. Upon completion of this course the student will be able to: generate systems specifications from a perceived need; partition functionality between hardware and software; produce interface specifications for a system composed of numerous subsystems; use computer-aided design tools; fabricate, integrate, and debug a hardware/software system; and evaluate the system in the context of a real world end user application.
PREREQUISITES: Senior standing in an engineering or science discipline
TEXT: There will be no text.
EVALUATION: The course is taught at a graduate level. Students design and implement a system, and submit oral and written monthly progress reports, and an oral and written final report with project demonstrations.
In order to receive coreunit credit CS graduate students will take a major roll in project leadership, which includes requirements generation, architecture design, implementation, and system integration. They will also be expected to tackle the more difficult programming and implementation problems. This is insured by the course staff which meets weekly to review progress and make plans for the next week.
COURSE TOPICS: The course is divided into four major phases, each composed of several subphases. The phases and their subphases are: Conceptualization (problem definition, technology survey); Planning (system architecture specification, subsystem specification); Design; and Implementation (implementation, system integration, and methodology evaluation).
15-828(A) Reconfigurable Computing
Cross-listed as 18-847
Goldstein/Schmit
MW 1:30-2:50, WeH 5409B
1 Coreunit (Pending DRC approval), 12 University Units
ENROLLMENT LIMIT: 20 students TOTAL
DESCRIPTION: This course will cover topics in reconfigurable computing, including FPGAs, architectures, compiler techniques, and applications. Each week, two or three papers will be handed out and assigned for reading and review. Two labs will be used to introduce the tool chains and current architectures used for reconfigurable computing. Students will also work on individual class projects.
PREREQUISITES: 18-760, 18-347 or 15-347, or permission of instructor.
METHOD OF EVALUATION: Grading will be based on class presentations, paper reviews, two lab projects and a class project.
TEXT: There will be no text, but papers and scribed course notes will be distributed.
SYLLABUS:
15-842(A) Advanced Storage and File Systems
Gibson
TR 1:30-2:50, WeH 3420
1 Coreunit (approved), 12 University Units
ENROLLMENT LIMIT: 20 students
DESCRIPTION: This course will review the design and implementation of advanced storage and file systems with a particular question in mind: what is the potential and appropriate use of programmability in storage devices? Readings and lecture material will cover the breadth of technologies bearing on the outcome of this question. Class projects will address open questions in order that the collected project reports provide a preliminary answer to the principle question, whither active disks. Examples of possible class projects include: active disk application development, remote execution infrastructure implementation, mechanisms and reasoning for global resource management, integration of active networks and active disks, or drive timesharing/thrashing/admission control.
PREREQUISITES: 15-412 and 18-349 or equivalent; or permission of instructor
METHOD OF EVALUATION: Class project and report, midterm (no final) exam, comparative review of a set of papers, one or two paper presentations, and class participation.
TEXT: Class readings will be handed out. There is no textbook.
SYLLABUS:
15-854 Machine Learning Theory
Blum
MW 1:30-2:50, WeH 4615A
1 Coreunit (approved), 12 University Units
DESCRIPTION: The general area of machine learning is a melting pot of ideas from a wide variety of disciplines. This course will focus on the theoretical side of machine learning. We will address questions such as: What kinds of guarantees can one prove about learning algorithms? What could one hope to prove? What are good models that are both amenable to mathematical analysis and make sense empirically? Can we use these models to come up with improved algorithms? What can we say about the inherent ease or difficulty of learning problems? Addressing these questions will require pulling in notions and ideas from statistics, complexity theory, cryptography, and on-line algorithms, as well as empirical machine learning and neural network research.
Specific topics to be covered include:
TEXT: Kearns and Vazirani, "An introduction to computational learning theory" plus papers and notes for topics not in the book. (Roughly half of the topics are in the book)
PREREQUISITES: Ideally students should either have an algorithms background with an interest in how those tools can be applied to a new domain, or should have a machine learning background with an interest in finding out some of the theoretical tools and ideas that have been developed. No specific courses are required. The algorithms core or 15-681 would be sufficient.
EVALUATION AND RESPONSIBILITIES: Grading will be based on a collection of (probably 6) homework assignments (60%), a (probably take-home) final exam (20%), and class participation (20%). As part of class participation, students will each give one presentation on a topic chosen in consultation with the instructor. Students interested in performing an experimental project based on some of the ideas discussed may be able to do so in place of some of the formal requirements. Because this course has no TA, students from time to time will also be asked to help with the grading of assignments.
15-882: Introduction to Artificial Neural Networks
Touretzky
MW 3:00 - 4:20 PM, WeH 4615A
1 Coreunit (approved), 12 University Units
DESCRIPTION: An introduction to neural networks for computer scientists and engineers. No previous exposure to the field is assumed. The course will include hands-on experience with a variety of neural net architectures modeled in MATLAB, and an in-depth look at problems in pattern recognition and knowledge representation.
List of material to be covered:
TEXTBOOK:
METHOD OF EVALUATION: Homework sets, and midterm and final exams.
PREREQUISITES: Undergrad calculus and linear algebra; solid programming skills.
05-810 / 88-770 Computer Supported Collaborative Work
Robert Kraut and William Scherlis
R 12:00-2:50, PH A19D
1 Coreunit Requested
12 University Units
Description: Most of the work that people do requires some degree of coordination and communication with others --- some kind of teamwork. But the development of technology to support teamwork has proven to be a considerable challenge in practice. Successful designs require (1) Social psychological insight into group processes, (2) Computer science insight into mechanisms to achieve coordination, sharing, and communication, and (3) HCI design insight. The course focuses on the first two of these factors, examining them in the context of a number of examples of teamwork systems.
In the course, problems in group coordination and the classes of systems attempting to overcome these problems are examined. We include (1) group decision support systems, (2) organizational memory systems, (3) video conferencing systems, (4) systems for creating virtual spaces, and (5) work flow and other sytems to structure interaction. For each topic area, we consider relevant theoretical and empirical results concerning group behavior from social psychnology and related fields which should inform sytem design, including basic phenomena of group behavior such as social loafing, the effect of video, the use of MUDs, communication and memory within organizations, group decision making, and large electronic groups.
We also examine technical topics related to the design and implementation of these systems, including management of shared objects (distributed object models, interpretation, versioning, concurrency control), communication and awareness (asynchronous and synchronous), workflow and notification (event models), and conventional shared structures (such as hypertext, databases, MUD rooms, discourse structure, and shared document databases). These topics are familiar themes in computer science; the treatment in the course focuses on their manifestation in the design of teamwork support systems. For example, locking strategies for collaborative systems can be more permissive than those used in traditional databases and operating systems.
The course has a research focus; students should leave with a clearer idea about important research topics in CSCW and will have conducted a preliminary project of their own. Many of the readings will be drawn from the recent CSCW conference proceedings. The course is suitable for both social science and engineering-oriented graduate students.
Topics to be covered:
Prerequisites: Graduate student standing or permission of instructor.
Texts:
Method of Evaluation: Students are evaluated on the basis of (1) a project, which involves either an empirical evaluation study or experimental engineering of some aspect of group interaction support. Students write up their reports as if for submission to the CHI or CSCW conferences. Most projects are conducted by groups, comprised of both social science and engineering students. The project and associated presentations represents 60% of the grade. Evaluation will also be based on (2) homeworks, which may involve small programming or analysis tasks, (3) individual class presentations, and (4) class participation. Class sessions revolve around several study questions and a short written assignment distributed the previous week.
Detailed syllabus: Last year's syllabus.
05-830 User Interface Software
Brad Myers
1 Coreunit Approved
12 University Units
Maximum Enrollment: 20
DESCRIPTION: After a quick overview of the design of user interfaces, we will concentrate on how to implement the chosen design. Particular emphasis will be placed on user interface software tools, such as windowing systems, toolkits, interface builders, prototypers, and advanced user interface development environments. In particular, the course will cover MS Windows, OLE, MFC, Macintosh Toolbox, MacApp, PowerPlant, OpenDoc, X/11, Motif, Visual Basic, Director, HyperCard, Java AWT, Java Beans, and various research systems like Amulet, InterViews, Fresco, and subArctic. Lectures will discuss the fundamental principles behind all of these systems, while showing the historical progression of the ideas from research prototypes to commercial systems. Today's research topics and open issues in user interface software will be emphasized throughout. Homeworks will primarily consist of each student implementing a small project on at least 4 different user interface tools during the semester, in order to experience first-hand the breadth of techniques used in modern tools. Students will all use an interactive prototyping tool first, like HyperCard, Director, Visual Basic or Delphi. Then each student will implement the same interface in three other "high-level" tools, which will be chosen so that the full range of tools is covered by members of the class. Students will study and use the "usability engineering" development methodology and will compare and evaluate the various tools for ease of learning and effectiveness.
CS PhD students in the class will be expected to learn and to do more exploratory work than the other students taking this course. (Some of what they will learn is covered in other HCI courses.) In particular, they will be asked to refine their specifications and perform iterative design based on user testing of the designs, prototypes, and implementations. They will also help present some of the course material by investigating and lecturing on various toolkits.
Required texts: None. If you have no prior HCI course, you SHOULD buy the Nielsen text below.
Recommended texts:
Prerequisites: 15-212 or considerable programming experience. Experience with object-oriented programming and/or software engineering is desirable. Prior experience with user interface design is NOT required.
Grading: The grades will be based on homeworks, a midterm, and final exam.
Pass/Fail available ONLY for CS PhD students.
Course Schedule: The schedule and topic list will be similar to last year's.
11-741/15-886 Information Retrieval
Yiming Yang
TR 1:30-2:50, Cyert Hall, Blue Conference Room
1 Coreunit Approved
12 University Units
DESCRIPTION: The graduate IR core course focuses on fundamental techniques for information retrieval, as well as introduce new challenging research areas in the field. The fundamental techniques include: document indexing, query processing, vector space models for document retrieval, probabilistic ranking, use of corpus statistics, scaling-up issues and evaluation. The new challenges include automatic learning of text classification, text clustering for summarization and discovery, cross-language and cross-media information retrieval, research issues in digital libraries and Internet, and so on.
PREREQUISITES:
The course is designed for Ph.D. students in LTI, at a difficulty level comparable to that of other LTI and CS graduate core courses for Ph.D. students. The course is also open to M.S. students at LTI or elsewhere if they have the prerequisites.
TEXT: There will be no official textbooks. Course notes and papers will be distributed instead. A reference book is available on-line: van Rijsbergen's "Information Retrieval". Many of the handouts will be available online. Some are photocopied hand-written slides which are not online.
METHOD OF EVALUATION: Grades will be based primarily (60%) on a multi-step project. The other components consist of a midterm (20%) and homework (20%). No final exam.
Homeworks:
Two problem sets and one 1 hands-on programming task, which contribute 20% of the total grade (as mentioned in the original proposal). Since substantial work is planned for the project part of this course, the homeworks will be not very time intensive.
To be more specific, the homeworks will contain 2 problem sets and one programming assignment; the assignments will focus on different aspects of text retrieval. Students are supposed to answer questions about the basic concepts and demonstrate their understanding of the techniques presented in class. These concepts will include: document indexing techniques, query expansion methods, term weighting schemes, use of corpus statistics, and different vector space models for document retrieval. The programming assignment will be to write programs to compute several of the standard retrieval performance measures, and apply these programs to evaluate an information retrieval system provided by the instructor.
Projects:
Students will choose from a set of projects designed by the instructor, including:
Students will also have the option of designing their own projects, subject to instructor approval. Projects are intended to give each student hands-on experience with state-of-the-art methods on challenging but tractable problems in Information Retrieval. A library of software and formatted data collections will be available on AFS to lighten the programming load. The software library will include several state-of-the-art search engines and statistical classifiers.
Projects will be evaluated by the following criteria:
TOPICS TO BE COVERED:
16-761(A) Introduction to Mobile Robotics
Illah Nourbakhsh
1 CSD Coreunit Approved
12 University Units
DESCRIPTION: This course presents a broad overview of the technologies and methods of mobile robotics. We provide this information using a historical context with the aim of giving the studentss the tools they need to develop a mature perspective on the field of mobile robotics.
At the same time, we stress both written and oral presentation skills by grading the students based on written critical reviews that they will provide and based on the final oral research project.
PREREQUISITES: There are no course prerequisites, although a strong mathematics foundation is required, as is intellectual enthusiasm.
TEXT: The course uses a broad set of reading materials, including more than 20 articles on robot systems and mobile robot technologies. In addition, we will employ selections from Autonomous Robot Vehicles by Cox and Wilfong.
METHOD OF EVALUATION: Students will be evaluated based on their scores on two writing projects, one final examination and two in-class oral presentations.
TOPICS TO BE COVERED: (See syllabus for a complete listing)
SYLLABUS:
Here is the complete syllabus for the course:
| Week | Date | Topics | Essays Due | ||||||||||||||||||||||||||||||||||||||||||||||
| 1 Tues | 1/13 | Introduction to Mobile Robotics | |||||||||||||||||||||||||||||||||||||||||||||||
| 2 Tues | 1/20 | Shakey: 1969; Robot Systems | |||||||||||||||||||||||||||||||||||||||||||||||
| 2 Thur | 1/22 | Stanford Cart: 1974; Robot Systems | |||||||||||||||||||||||||||||||||||||||||||||||
| 3 Tues | 1/27 | Sensors I | |||||||||||||||||||||||||||||||||||||||||||||||
| 3 Thurs | 1/29 | Mobi: 1987; RTStereo: 1984; Sensors I | |||||||||||||||||||||||||||||||||||||||||||||||
| 4 Tues | 2/3 | Critical Writing and Presentation Skills | |||||||||||||||||||||||||||||||||||||||||||||||
| 4 Thur | 2/5 | Terregator: 1984 Effectors I | |||||||||||||||||||||||||||||||||||||||||||||||
| 5 Tues | 2/10 | Effectors I | |||||||||||||||||||||||||||||||||||||||||||||||
| 5 Thurs | 2/12 | Potential Fields: 1986; Control I | |||||||||||||||||||||||||||||||||||||||||||||||
| 6 Tues | 2/17 | Brooks: 1986; Decomposition | |||||||||||||||||||||||||||||||||||||||||||||||
| 6 Thur | 2/19 | RAPS: 1987 Decomposition; Simulation returns | |||||||||||||||||||||||||||||||||||||||||||||||
| 7 Tues | 2/24 | Vagabond 1992; Motion Planning | |||||||||||||||||||||||||||||||||||||||||||||||
| 7 Thur | 2/26 | People Recognizer 1994; The Role of AI | |||||||||||||||||||||||||||||||||||||||||||||||
| 8 Tues | 3/3 | Universal Plans 1987; Deliberate Systems | Writing Assign | ||||||||||||||||||||||||||||||||||||||||||||||
| 8 Thur | 3/5 | Anytime Plans 1988; Planning and Execution
| 9 Tues
| 3/10
| Xavier 1995; Navigation
| 9 Thur
| 3/12
| Polly 1991; Sensors II
| 10 Tue
| 3/17
| Navlab 1993; Sensors II
| 10 Thur
| 3/19
| Uranus 1992; Effectors II
| 11 Tue
| 3/31
| Autonomous Helicopter 1996; Complex Systems
| Critical TR 1.1
| 11 Thur
| 4/2
| Deep Space One 1996; Complex Systems
| 12 Tue
| 4/7
| Helpmate 1995; Complex Systems
| 12 Thur
| 4/9
| Cybermotion & Nomadics; Robot Learning
| 13 Tue
| 4/14
| Inductobeast 1996;Robot Learning
| 13 Thur
| 4/16
| Walking Robots: leg control, gaits
| 14 Tue
| 4/21
| Robocup; Multiple Robots & Robot Teams
| 14 Thur
| 4/23
| Hyperbot; Educational Mobile Robots
| 15 Tue
| 4/28
| Final presentations
| Final Ver, Critical TR
| 15 Thur
| 4/30
| Final Presentations
| 16 ?
|
| Final Essay Examination
| |
15-499(E) / 60-422(A) Robotic Art Studio
Simon Penny
MW 6:30-9:20
1 Coreunit Requested
10 University Units
DESCRIPTION: Robotic Art Studio is a hybrid course which examines the aesthetic applications of embedded microprocessors and robotic technologies. The goal of the class is to determine what constitutes a robotic artwork to build such a thing and present it in public exhibition. Thus central theoretical questions include basic consideration of the nature of art, how an artwork functions cognitively and culturally, and particularly the problematics of an aesthetics of interaction, with an emphasis on embodied, kinesthetic interaction. As projects are realised colaboratively, the process of group formation and project development, management of tasks and personality issues, design, specification of components, prototyping and testing are all critical. Technical training emphasises practical employment of a wide variety of technologies, including component electronics, basic mechanics, machine design and fabrication, programming embedded microcontrollers.
PREREQUISITES: None.
TEXTS:
TOPICS TO BE COVERED:
Complete syllabus available from Sharon Burks.