Abstract Missing

A key question in vision is how to represent our knowledge of previously encountered objects to classify new ones. The answer depends on how we determine the similarity of two objects. Similarity tells us how relevant each previously seen object is in determining the category to which a new object belongs.

The WS (Wake-Sleep) algorithm is a simple learning rule for the models with hidden variables. It is shown that this algorithm can be applied to a factor analysis model which is a linear version of the Helmholtz machine. Buteven for a factor analysis model, the general convergence is not proved theoretically. In this article, we describe the geometrical understanding ofthe WS algorithm in contrast with the EM (Expectation Maximization) algorithm and the em algorithm. As the result, we prove the convergence of the WS algorithm for the factor analysis model. We also show the condition for the convergence in general models.

Recent works in parameter estimation and neural coding have demonstrated that optimal performance are related to the mutual information between parameters and data. We consider the mutual information in the case where the dependency in the parameter (a vector 8) of the conditional p.d.f. of each observation (a vector

Multi-Agent Systems (MAS) promise to offer solutions to problems where established, older paradigms fall short. In order to validate such claims that are repeatedly made in software agent publications, empirical in-depth studies of advantages and weaknesses of multi-agent solutions versus conventional ones in practical applications are needed. Climate control in large buildings is one application area where multi-agent systems, and market-oriented programming in particular, have been reported to be very successful, although central control solutions are still the standard practice. We have therefore constructed and implemented a variety of market designs for this problem, as well as different standard control engineering solutions. This article gives a detailed analysis and comparison, so as to learn about differences between standard versus agent approaches, and yielding new insights about benefits and limitations of computational markets. An important outcome is that ``local information plus market communication produces global control''.

The "Journal of Artificial Intelligence Research (JAIR) was one of the first scientific journals distributed over the web. It has now completed over five years of successful publication. Electronic publishing is reshaping the way academic work is disseminated, and JAIR is leading the way toward a future where scientific articles are freely and easily accessible to all. This report describes how the journal has evolved, its "grassroots" philosophy, and prospects for the future.

The research on conditional planning rejects the assumptions that there is no uncertainty or incompleteness of knowledge with respect to the state and changes of the system the plans operate on. Without these assumptions the sequences of operations that achieve the goals depend on the initial state and the outcomes of nondeterministic changes in the system. This setting raises the questions of how to represent the plans and how to perform plan search. The answers are quite different from those in the simpler classical framework. In this paper, we approach conditional planning from a new viewpoint that is motivated by the use of satisfiability algorithms in classical planning. Translating conditional planning to formulae in the propositional logic is not feasible because of inherent computational limitations. Instead, we translate conditional planning to quantified Boolean formulae. We discuss three formalizations of conditional planning as quantified Boolean formulae, and present experimental results obtained with a theorem-prover.

Victor Lesser (University of Massachusetts at Amherst) gave an invited talk on distributed interpretation and its of Hong Kong Polytechnic University, possible implication in DDM. Mining, brought interested researchers (Brigham Young University) and Salvatore The paper sessions ended with two and practitioners together and created Stolfo (Columbia University) working paper presentations by Billy an environment for crystallizing the presented the effects of class distribution Wallace and Juan Botia, Marcedes Garijo, fast-growing field of DDM. The concluding session was the panel Lawrence Hall, Nitesh Chawla, and 40 participants attended the workshop. Stolfo, George Cybenko Kevin W. Bowyer (all of University of The workshop had 13 presentations, Stolfo stressed suggested different techniques for Cybenko of Dartmouth University. Organizers sincerely hope that the session.

We describe an application of a dynamic replanning technique in a highly dynamic and complex domain: the military aeromedical evacuation of patients to medical treatment facilities. U.S. Transportation Command (USTRANSCOM) is the U.S. Department of Defense (DoD) agency responsible for evacuating patients during wartime and peace. Doctrinally, patients requiring extended treatment must be evacuated by air to a suitable medical treatment facility. The Persian Gulf War was the first significant armed conflict in which this concept was put to a serious test. The results were far from satisfactory -- about 60 percent of the patients ended up at the wrong destinations. In early 1993, the DoD tasked USTRANSCOM to consolidate the command and control of medical regulation and aeromedical evacuation operations. The ensuing analysis led to TRAC2ES (TRANSCOM regulating and command and control evacuation system), a decision support system for planning and scheduling medical evacuation operations. Probably the most challenging aspect of the problem has to do with the dynamics of a domain in which requirements and constraints continuously change over time. Continuous dynamic replanning is a key capability of TRAC2ES. This article describes the application and the AI approach we took in providing this capability.

The study of belief change has been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. In a companion paper (Friedman & Halpern, 1997), we introduce a new framework to model belief change. This framework combines temporal and epistemic modalities with a notion of plausibility, allowing us to examine the change of beliefs over time. In this paper, we show how belief revision and belief update can be captured in our framework. This allows us to compare the assumptions made by each method, and to better understand the principles underlying them. In particular, it shows that Katsuno and Mendelzon's notion of belief update (Katsuno & Mendelzon, 1991a) depends on several strong assumptions that may limit its applicability in artificial intelligence. Finally, our analysis allow us to identify a notion of minimal change that underlies a broad range of belief change operations including revision and update.