We propose a learning dynamics to model how strategic agents repeatedly play a continuous game while relying on an information platform to learn an unknown payoff-relevant parameter. In each time step, the platform updates a belief estimate of the parameter based on players' strategies and realized payoffs using Bayes's rule. Then, players adopt a generic learning rule to adjust their strategies based on the updated belief. We present results on the convergence of beliefs and strategies and the properties of convergent fixed points of the dynamics. We obtain sufficient and necessary conditions for the existence of globally stable fixed points. We also provide sufficient conditions for the local stability of fixed points. These results provide an approach to analyzing the long-term outcomes that arise from the interplay between Bayesian belief learning and strategy learning in games, and enable us to characterize conditions under which learning leads to a complete information equilibrium.

The workshop was organized by Jack Minker and John McCarthy. The Program Committee members were Krzysztof Apt, John Horty, Sarit Kraus, Vladimir Lifschitz, John McCarthy, Jack Minker, Don Perlis, and Ray Reiter. The purpose of the workshop was to bring together researchers who use logic as a fundamental tool in AI to permit them to review accomplishments, assess future directions, and share their research in LBAI. This article is a summary of the workshop. The areas selected for discussion at the workshop were abductive and inductive reasoning, applications of theorem proving, commonsense reasoning, computational logic, constraints, logic and high-level robotics, logic and language, logic and planning, logic for agents and actions, logic of causation and action, logic, probability and decision theory, nonmonotonic reasoning, theories of belief, and knowledge representation.

This article describes a methodology for programming robots known as probabilistic robotics. The probabilistic paradigm pays tribute to the inherent uncertainty in robot perception, relying on explicit representations of uncertainty when determining what to do. This article surveys some of the progress in the field, using in-depth examples to illustrate some of the nuts and bolts of the basic approach.

To assess the state of the art and identify issues requiring further investigation, a workshop on qualitative and abstract probability was held during the third week of November 1993. This workshop brought together a mix of active researchers from academia, industry, and government interested in the practical and theoretical impact of these abstractions on techniques, methods, and tools for solving complex AI tasks. The result was a set of specific recommendations on the most promising and important avenues for future research. The workshop, entitled "Putting Qualitative and Abstract Probability to Work," gathered active researchers from university, industry, and government to assess the state of the art and make recommendations for future research. The event was sponsored by the Palo Alto Laboratory of Rockwell Science Center.

Part of the Media Laboratory's heritage (its origins are in the School of Architecture) is a startling receptivity to the arts, especially music and the visual arts, and Brand repeatedly returns to this subject. Even here, intellectualism reigns: It is symptomatic that the lab members' interest in literature seems to be limited to science fiction. This lopsidedness echoes Turkle's complaint that hackers ignore the texture (emotion) of music in favor of its structure (intellect). Not an engineer himself, Brand is not always in a position to critically evaluate what he saw; I was reminded of persons who, on seeing ELIZA, concluded that computerized psychotherapy was just around the corner. As Brand points out, the Media Lab replaces the publish-orperish imperative with demo or die, and anyone who has produced a demo knows something about practical mendacity.

The t.estbed simulates a class of a distributed knowledge-based THERE ARE TWO MAJOR T IEMES of this article. First, WC introduce readers to the emerging subdiscipline of AI called Dzstrrbuted Problem Solving, and more specifically the authors' research on Functionally Accurate, Cooperative systems Second, we discuss the st,ructure of tools that allow more thorough experimentation than has typically been performed in AI research An examplr of such a tool, the Distributed Vehicle Monitoring Testbed, will bc presented. The testbed simulates a class of dist,ributed knowledge-based problem solving systems operating on an abstracted version of a vehicle monitoring task. This presentation emphasizes how the t,estbed is structured to facilit,ate the study of a wide range of issues faced in t,he design of distributed problem solving networks. Distribut,ed Problem Solving (also called Distributed Al) combines the research interests of the fields of AI and Distributed Processing (Chandrasekaran 1981; Davis 1980, 1982; Fehling & Erman 1983).

Li is not small compared to that of A. However, To understand how this rule works, let us return to the submarine example and assume that there are two groups of experts El,..., As is pointed out in Zadeh (1979a), the Dempster rule P*(notA) 1. This, in a nutshell, is the basic idea underly-of combination of evidence may lead to counterintuitive coning the Dempster-Shafer theory. The An important observation is in order at this juncture. P(A), that S is in A, the answer would be (after the object under consideration does not exist. P*(A) are the degrees of belief and plausibility associated of evidence, consider the following situation.

Genetic Epistemology Editor: In his recent article in AI Magazine, "AI prepares for 2001," Nils Nilsson put forward a paradigm of AI based on a declarative representation of knowledge with semantic attachments to problem-specific procedures and data structures. The author discussed various research strategies for AI and specifically a computer-individual project was introduced as an efficient way of stimulating research and advances in the basic science of AI The undertaking of such a project immediately raises some classical psychological questions. Besides the deductive versus inductive or declarative versus procedural controversials, problems related to knowledge representation and evolution in an interactive environment must be considered. I would like to present some ideas and concepts stemming from current research in Genetic Epistemology (GE), initiated by Jean Piaget, as possible contributions to AI research fields. Knowledge is a common preoccupation for GE and AI.

The Second International Workshop on User Modeling was held March 30-April 1, 1990 in Honolulu, Hawaii. The general chairperson was Dr. Wolfgang Wahlster of the University of Saarbrucken; the program and local arrangements chairperson was Dr. David Chin of the University of Hawaii at Manoa. The workshop was sponsored by AAAI and the University of Hawaii, with AAAI providing eight travel stipends for students. An excellent response to the call for papers and participants resulted in 46 high quality submissions, of which 24 were selected for presentation and discussion led by invited commentators. Whereas the first user modeling workshop, held in Maria Laach, West Germany in 1986, focused on user modeling in natural language dialogue systems, the 1990 workshop covered a broader range of topics, including user modeling in tutoring systems and psychological foundations of user modeling.

The SNePS knowledge representation, reasoning, and acting system has several features that facilitate metacognition in SNePSbased agents. The most prominent is the fact that propositions are represented in SNePS as terms rather than as sentences, so that propositions can occur as arguments of propositions and other expressions without leaving first-order logic. The SNePS acting subsystem is integrated with the SNePS reasoning subsystem in such a way that: there are acts that affect what an agent believes; there are acts that specify knowledge-contingent acts and lack-ofknowledge acts; there are policies that serve as "daemons," triggering acts when certain propositions are believed or wondered about. The GLAIR agent architecture supports metacognition by specifying a location for the source of self-awareness and of a sense of situatedness in the world. Several SNePSbased agents have taken advantage of these facilities to engage in self-awareness and metacognition.