Our program in AI is part of the larger departmental focal area of cybernetics which integrates both AI and brain theory (BT). Our research also draws upon a new and expanding interdepartmental program in cognitive science that brings together researchers in cybernetics, linguistics, philosophy, and psychology. This interdisciplinary approach to AI has already led to a number of fruitful collaborations in the areas of cooperative computation, learning, natural language parsing, and vision.

Summary of the Executive Council meeting held 26 August 1981 in Vancouver, British Columbia.

Amidst the beautiful foliage in Jackson, N.H., the each session circulated a position paper to the group, Second KL-ONE Workshop was held over a five-day raising the questions he wanted to see addressed at the period this past October. Not only did "KloneTalk" (a version of KL-ONE implemented in we have a general conference session, wherein people SmaiiTa k at Xerox PARC -- this inchrded a videotaped could report on activities at their own institutions, discuss demonstration of the system's interface), prototypes in issues of general interest, etc., we also had a knowledge representation, translation of INTERLISP two-and-a-half day working research session. KL-ONE to FranzLisp, a calculus of Structural The technieai discussion part of the Workshop Descriptions, and the KL-ONE Classifier, not to mention several others. We also had the larger group break up preceded the general conference, so that we could report into smaller working groups to consider inference in on findings to the larger group of participants (forty-six KL-ONE, representing beliefs, some KL-ONE practice this year, from twenty-one institutions). Also, we planned to cover only a small extensive Proceedings of the Workshop.

Consider a class of computing problem for which all bananas is left as an exercise for the reader, or the sufficiently short programs are too slow and all sufficiently monkey. When it has been possible to couple causal models problems of this kind were left strictly alone for the first with various kinds and combinations of search, twenty-years or so of the computing era. There were two mathematical programming and analytic methods, then good reasons. First, the above definition rules out both evaluation of t has been taken as the basis for "high road" the algorithmic and the database type of solution. In "low road" representations Second, in a pinch, a human expert could usually be s may be represented directly in machine memory as a set found who was able at least to compute acceptable A recent pattern-directed allocation, inventory optimisation, or whatever large heuristic model used for industrial monitoring and control combinatorial domain might happen to be involved.

Cambridge University Press

ACM Computing Surveys, 14(l), March

Robot Motion is included in theMIT Press Artificial Intelligence Series.

Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.

We briefly describe the logic programming language PROLOG concentrating on those aspects of the language that make it suitable for implementing expert systems. We show how features of expert systems such as: (1) inference generated requests for data, (2) probabilistic reasoning, (3) explanation of behaviour can be easily programmed in PROLOG. We illustrate each of these features by showing how a fault finder expert could be programmed in PROLOG.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.

"The purpose of this paper is to compare various approaches to generalization in terms of a single framework. Toward this end, generalization is cast as a search problem, and alternative methods for generalization are characterized in terms of the search strategies that they employ. This characterization uncovers similarities among approaches, and leads to a comparison of relative capabilities and computational complexities of alternative approaches. The characterization allows a precise comparison of systems that utilize different representations for learned generalizations."Artificial Intelligence, 18 (2), 203-26.