Date: Fri 8 Jan 1988 22:19-PST From: AIList Moderator Kenneth Laws Reply-To: AIList@SRI.COM Us-Mail: SRI Int., 333 Ravenswood Ave., Menlo Park, CA 94025 Phone: (415) 859-6467 Subject: AIList V6 #4 - Seminars, Conferences To: AIList@SRI.COM Status: RO AIList Digest Saturday, 9 Jan 1988 Volume 6 : Issue 4 Today's Topics: Seminars - Recovery From Incorrect Knowledge In SOAR (GMR) & Open-Ended Learning Through Machine Evolution (Siemens), Conference - 2nd Workshop on Qualitative Physics & Neural Controls Session at ACC ---------------------------------------------------------------------- Date: Mon, 4 Jan 88 11:38 EST From: "R. Uthurusamy" Subject: Seminar - Recovery From Incorrect Knowledge In SOAR (GMR) Seminar at the General Motors Research Laboratories in Warren, Michigan. Wednesday, January 20, 1988 at 10 a.m. RECOVERY FROM INCORRECT KNOWLEDGE IN SOAR JOHN E. LAIRD Assistant Professor, Electrical Engineering and Computer Science Dept. The University of Michigan ABSTRACT: In previous work, we have demonstrated some of the generality of Soar's problem solving and learning capabilities. We even gone so far as to hypothesize that the simple learning mechanism in Soar, chunking, combined with its general problem solving capabilities, is sufficient for all cognitive learning. This is a radical hypothesis especially when we consider Soar's difficulty with recovery from incorrect knowledge. Soar acquires incorrect knowledge whenever it chunks over invalid inductive inferences made during problem solving. Recovery requires some form of identification and correction of the incorrect knowledge. Recovery is complicated in Soar by the fact that we have made the following assumptions: chunking is the only learning mechanism; long-term knowledge, represented as production rules, is only added, never forgotten, modified or replaced; and the productions are not open for direct examination by the learning mechanism or the problem solver. In this talk I will review chunking in Soar and present recent results in developing a domain-independent approach for the recovery from incorrect knowledge in Soar. This approach does not require any change to the Soar architecture, but uses chunking to learn rules that overcome the incorrect knowledge. The key is to use the problem solving to deliberately reconsider decisions that might be in error. If a decision is found to be incorrect, the problem solving corrects it and a new chunk is learned that will correct the decision in the future. Non-GMR personnel interested in attending this seminar please contact R. Uthurusamy [ samy@gmr.com ] 313-986-1989 ------------------------------ Date: 7 Jan 88 00:39:29 GMT From: siemens!hudak@princeton.edu (Michael J. Hudak) Subject: Seminar - Open-Ended Learning Through Machine Evolution (Siemens) Speaker: Peter Cariani Systems Science Dept., Thomas J. Watson School of Engineering State University of New York at Binghamton Title: Structural Preconditions for Open-Ended Learning through Machine Evolution Location: Siemens Corporate Research & Support, Inc. 3rd floor Multi-Purpose Room Princeton Forrestal Center 105 College Road East Princeton, NJ 08540-6668 Date: Thursday, 14 January 1988 Time: 10:00 am (refreshments: 9:45) For more information call Mike Hudak: 609/734-3373 Abstract One of the basic problems confronting artificial life simulations is the apparent open-ended nature of structural evolution, classically known as the problem of emergence. Were it possible to construct devices with open-ended behaviors and capabilities, fundamentally new learning tech- nologies would become possible. At present, none of our devices or models are open-ended, due to the nature of their design and construction. The best devices we have, in the form of trainable machines, neural net simulations, Boltzmann machines and Holland-type adaptive machines, exhibit learning within the categories fixed by their feature spaces. Learning occurs through the performance dependent optimization of alter- native I/O functions. Within the adaptive machine paradigm of these devices, the measuring devices, feature spaces, and hence the real world semantics of such devices are stable. Such machines cannot create new primitive categories; they will not expand their feature and behavior spaces. Over phylogenetic time spans, however, organisms have evolved new sensors and effectors, allowing them to perceive more and more aspects of their environments and to act in more and more ways upon those environments. This involves a whole new level of learning: the learning of new primitive cognitive and behavioral categories. In terms of constructible devices, this level of learning encompasses machines which construct and select their own sensors and effectors, based upon their real world performance. The semantics of the feature and behavior spaces of such devices thus changes so as to optimize their effectiveness as categories of perception and action. Such devices construct their own primitive categories, their own primitive concepts. Evolutionary devices could be combined with adaptive ones to both optimize primitive categories and I/O mappings within those categories. Evolutionary machines cannot be constructed through computations alone. New primitive category construction necessitates that new physical measuring structures and controls come into being. While the behavior of such devices can be represented to a limited degree by formal models, those models cannot themselves create new categories vis-a-vis the real world, and hence are insufficient as category-creating devices in their own right. Computations must be augmented by the physical construction of new sensors and effectors implementing processes of measurement and control respectively. This construction process must be inheritable and replicable, hence encodable into symbolic form, yet involving the autono- mous, unencoded dynamics of the matter itself. The paradigmatic example of a natural construction process is protein folding. A one-dimensional string of nucleotides, itself a discrete, rate-independent symbolic structure, is transformed into continuous, rate dependent dynamics having biological function through the action of the physical properties inherent in the protein chain itself. The functional properties of speed, specificity, and reliability of action are thus achieved with symbolic constraints but without the explicit direction of rules. ------------------------------ Date: Tue, 5 Jan 88 15:50:05 CST From: forbus@p.cs.uiuc.edu (Kenneth Forbus) Subject: Conference - 2nd Workshop on Qualitative Physics CALL FOR PARTICIPATION SECOND WORKSHOP ON QUALITATIVE PHYSICS PARIS, JULY 26-28, 1988 Following last year's success of the first workshop on Qualitative Physics organized by the Qualitative Reasoning Group at the University of Illinois (with AAAI sponsorship), the second workshop on Qualitative Physics will be organized by the European Group on Qualitative Physics and the IBM Paris Scientific Center. The workshop, sponsored by the Commission of the European Community (JRC-Ispara) and in cooperation with AAAI, will be held in Paris on July 26-28, 1988. It is intended as a forum for discussion of ongoing research in Qualitative Physics and related areas. To develop interaction and exchange of ideas, a number of panels will be organized. We invite proposals for panels on ongoing debates in the area, such as: -- Causal Reasoning -- Mathematical Aspects of Qualitative Models -- Naive Physics versus Qualitative Physics Another suggested panel format is to pose a particular problem which panelists must use to focus discussion. Proposers for panels should obtain the agreement of the panelists and submit the proposal, including an outline of the suggested discussion, to the program chairman by March 8, 1988. ATTENDANCE: To encourage lively discussion, attendance will be by invitation only. If you are interested in attending, please submit five (5) copies of an extended abstract, up to 6 pages long, to the program chairman: Francesco Gardin Dipartimento di Scienze dell'Informazione, Universita degli Studi di Milano Via Moretto da Bresica, 9 20133 Milano, ITALY tel. +39-2-2141230 The deadline for submissions is MARCH 8th, 1988 and invitations will be mailed APRIL 5th, 1988. Abstracts will be reviewed by an international scientific committee. Results already submitted for publication elsewhere are acceptable since no proceedings of the workshop will be published. A subset of the authors may be asked to contribute to a book based on the workshop. Besides presenters of papers, a limited number of observers may be accepted. For further information about the organization of the workshop, contact any member of the organizing committee, or: Olivier Raiman IBM Paris Scientific Center 3/5 Place Vendome, 75001 Paris, FRANCE tel. +33-1-4296-1475 ==================== ORGANIZING COMMITTEE Johan De Kleer (Xerox PARC) Ken Forbus (University of Illinois, Urbana) Pat Hayes (Xerox PARC), Ben Kuipers (University of Texas, Austin) and all the members of the European Qualitative Physics Committee: Flavio Argentesi (JRC-Ispra) Ivan Bratko (University of Ljubljana) John Campbell (Univ. College of London) Jean-Luc Dormoy (EDF) Boi Faltings (E.P.F. Lausanne) Francesco Gardin (University of Milan) Bernd Hellingrath (Fraunhofer-Institute ITW) Roy Leitch (Heriot-Watt University) Nicools J. Mars (Univ. of Twente) Pierre Van Nypelseer (AITECH, Brussels) Olivier Raiman (IBM Paris Scientific Centre) Peter Struss (Siemens) ------------------------------ Date: Fri, 8 Jan 88 07:27 PST From: nesliwa%nasamail@ames.arc.nasa.gov (NANCY E. SLIWA) Subject: Conference - Neural Controls Session at ACC In response to requests for information about the ACC session in neural applications to robotics, about which I recently solicited names, I am posting the current status of the session, along with minimal conference information. Registration information can probably be obtained from the general chair. 1988 American Controls Conference June 15-17, 1988 The Atlanta Hilton and Towers Atlanta, Georgia General Chair: Wayne Book The George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta, Georgia 30332 (404) 894-3247 Invited Session on Neural Networks in Control (A 4-hour session, 8 regular papers) Chairs: Moshe Kam, Drexel University Don Soloway, NASA Langley Research Center "How Neural Networks Factor Problems of Sensory Motor Control" Danial Bullock, Boston University "Neural and Adaptive Control: Similarities and Differences" A. Sideris, D. Psaltis, A. Yamakura, California Inst. of Technology "On State Space Analysis for Neural Networks" Moshe Kam, Roger Cheng, Allon Guez, Drexel University "Adaptive Neural Model for Hand-Eye Coordination" M. Kuperstein, Wellesley College "A Neural Network for Planning Preshape Postures of the Human Hand" Thea Ibarall, University of Southern California "Strategy Learning with Multilayer Connectionist Representations" Charles Anderson, GTE Labs Inc. "Neural-Networks-Based Learning Systems for Material Handling Using Multiple Robots" D-Y Yeung, George Bekey, University of Southern California "Using Neural Networks to Characterize Complex Systems" Philip Daley, A. Thornbrugh, Martin-Marietta Astronautics Group Nancy Sliwa NASA Langley Research Center 804/865-3871 nesliwa%nasamail@ames.arpa ------------------------------ End of AIList Digest ********************