We present a new approach to the supervised learning of lateral interactions forthe competitive layer model (CLM) dynamic feature binding architecture. The method is based on consistency conditions, which were recently shown to characterize the attractor states of this linear threshold recurrent network. For a given set of training examples the learning problem isformulated as a convex quadratic optimization problem in the lateral interaction weights. An efficient dimension reduction of the learning problem can be achieved by using a linear superposition of basis interactions.

I derive approximations to the learning curves for the more generic case of mismatched models, and find very rich behaviour: For large input space dimensionality, where the results become exact, there are universal (student-independent) plateaux in the learning curve, with transitions in between that can exhibit arbitrarily many over-fitting maxima; over-fitting can occur even if the student estimates the teacher noise level correctly. In lower dimensions, plateaux also appear, and the learning curve remains dependent on the mismatch between student and teacher even in the asymptotic limit of a large number of training examples. Learning withexcessively strong smoothness assumptions can be particularly dangerous:For example, a student with a standard radial basis function covariance function will learn a rougher teacher function onlylogarithmically slowly. All predictions are confirmed by simulations. 1 Introduction There has in the last few years been a good deal of excitement about the use of Gaussian processes (GPs) as an alternative to feedforward networks [1]. GPs make prior assumptions about the problem to be learned very transparent, and even though they are nonparametric models, inference-at least in the case of regression considered below-is relatively straightforward. One crucial question for applications is then how'fast' GPs learn, i.e. how many training examples are needed to achieve a certain level of generalization performance.

Henry Lieberman surveyed successful techniques for programming by example, an approach where end users teach procedures to computers by demonstrating a sequence of actions on concrete examples as they how to accomplish it. This new conference series domain-independent inference practical exercises and illustrated promotes multidisciplinary research structures and reusable domain-specific the concepts with applications, including on tools and methodologies for efficiently ontologies. A related workshop of its knowledge content for communities. He received his Ph.D. in 1. portal.acm.org. For any inquiries, please email info@kcap.org.

February 10: IJCAI-03 Electronic After the conference, an expense report Mark your calendars now for IJCAI-poster submission deadline will be required to account for the 03! The Eighteenth International February 12: IJCAI-03 Hard-copy funds awarded.

MITAP (MITRE text and audio processing) is a prototype system available for monitoring infectious disease outbreaks and other global events. MITAP focuses on providing timely, multilingual, global information access to medical experts and individuals involved in humanitarian assistance and relief work. Multiple information sources in multiple languages are automatically captured, filtered, translated, summarized, and categorized by disease, region, information source, person, and organization. Critical information is automatically extracted and tagged to facilitate browsing, searching, and sorting. The system supports shared situational awareness through collaboration, allowing users to submit other articles for processing, annotate existing documents, post directly to the system, and flag messages for others to see. MITAP currently stores over 1 million articles and processes an additional 2,000 to 10,000 daily, delivering up-to-date information to dozens of regular users.

This article describes a method for building a cognitive map of a virtual urban environment. Our routines enable virtual humans to map their environment using a realistic model of perception. We based our implementation on a computational framework proposed by Yeap and Jefferies (1999) for representing a local environment as a structure called an absolute space representation (ASR). Their algorithms compute and update ASRs from a 2-1/2-dimensional (2-1/2D) sketch of the local environment and then connect the ASRs together to form a raw cognitive map.1 Our work extends the framework developed by Yeap and Jefferies in three important ways. First, we implemented the framework in a virtual training environment, the mission rehearsal exercise (Swartout et al. 2001). Second, we developed a method for acquiring a 2- 1/2D sketch in a virtual world, a step omitted from their framework but that is essential for computing an ASR. Third, we extended the ASR algorithm to map regions that are partially visible through exits of the local space. Together, the implementation of the ASR algorithm, along with our extensions, will be useful in a wide variety of applications involving virtual humans and agents who need to perceive and reason about spatial concepts in urban environments.

SOAR is one of the oldest and largest AI development efforts, starting formally in 1983. It has also been proposed as a unified theory of cognition (Newell 1990). Most of its current development is as an AI programming language, which was evident at the Twenty-Second SOAR Workshop held at Soar Technology near the University of Michigan in Ann Arbor on 1-2 June 2002.

AAAI's World Wide Web Site serves as a Effective for 1996 the Association has central resource for individuals involved in

This book looks at some of the results of this synergy among AI, cognitive science, and education. Examples include virtual students whose misconceptions force students to reflect on their own knowledge, intelligent tutoring systems, and speech recognition technology that helps students learn to read. Some of the systems described are already used in classrooms and have been evaluated; a few are still laboratory efforts. The book also addresses cultural and political issues involved in the deployment of new educational technologies. ISBN 0-0-262-56141-7 To order call 800-405-1619.

The chapters in this book examine the state of today's agent technology and point the way toward the exciting developments of the next millennium. Contributors include Donald A. Norman, Nicholas Negroponte, Brenda Laurel, Thomas Erickson, Ben Shneiderman, Thomas W. Malone, Pattie Maes, David C. Smith, Gene Ball, Guy A. Boy, Doug Riecken, Yoav Shoham, Tim Finin, Michael R. Genesereth, Craig A. Knoblock, Philip R. Cohen, Hector J. Levesque, and James E. White, among others. He then went on to outline that drive a field forward. Francisco's W Hotel, the conference not, to succeed when placed in front Along with the program committee, included work from researchers and of real users. He argued that we are the conference web site and online have faced increasingly challenging now living in a time where we can submissions and reviewing.