The general goal of this workshop was to bring t.ogether researchers working toward developing a theoretical framework for the analysis and design of neural networks. The t.echnical focus of the workshop was to address recent. The primary topics addressed the following three areas: 1) Computational complexity issues in neural networks, 2) Complexity issues in learning, and 3) Convergence and numerical properties of learning algorit.hms. Such st.udies, in t.urn, have generated considerable research interest. A similar development can be observed in t.he area of learning as well: Techniques primarily developed in the classical theory of learning are being applied to understand t.he generalization and learning characteristics of neural networks.

The general goal of this workshop was to bring t.ogether researchers working toward developing a theoretical framework for the analysis and design of neural networks. The t.echnical focus of the workshop was to address recent. The primary topics addressed the following three areas: 1) Computational complexityissues in neural networks, 2) Complexity issues in learning, and 3) Convergence and numerical properties of learning algorit.hms. Such st.udies, in t.urn, have generated considerable research interest. A similar development can be observed in t.he area of learning as well: Techniques primarily developed in the classical theory of learning are being applied to understand t.he generalization and learning characteristics of neural networks.

David A. Cohn MIT Dept. of Brain and Cognitive Sciences Cambridge, MA 02139 The goal of this workshop was to discuss two major issues: efficient exploration of a learner's state space, and learning in continuous domains. The common themes that emerged in presentations and in discussion were the importance of choosing one'sdomain assumptions carefully, mixing controllers/strategies, avoidance of catastrophic failure, new approaches with difficulties with reinforcement learning, and the importance of task transfer. He suggested that neither "fewer assumptions are better" nor "more assumptions are better" is a tenable position, and that we should strive to find and use standard sets of assumptions. With no such commonality, comparison of techniques and results is meaningless. Under Moore's guidance, the group discussed the possibility of designing an algorithm which used a number of well-chosen assumption sets and switched between them according to their empirical validity.

The goal of this workshop was to discuss two major issues: efficient exploration of a learner's state space, and learning in continuous domains. The common themes that emerged in presentations and in discussion were the importance of choosing one's domain assumptions carefully, mixing controllers/strategies, avoidance of catastrophic failure, new approaches with difficulties with reinforcement learning, and the importance of task transfer. He suggested that neither "fewer assumptions are better" nor "more assumptions are better" is a tenable position, and that we should strive to find and use standard sets of assumptions. With no such commonality, comparison of techniques and results is meaningless. Under Moore's guidance, the group discussed the possibility of designing an algorithm which used a number of well-chosen assumption sets and switched between them according to their empirical validity.

This report summarizes this year's workshop and outlines WET to underwrite and support these workshops. Information Systems is also acknowledged. The Defense Advanced this year's workshop and outlines the philosophy behind this annual event. Computer-Supported Cooperative and present the best research Finally, I would like to thank V. Work gathering, which takes in that has a bearing on the "repersonalization Jagannathan for his great help and everyone from anthropologists to of computing," as Fernando expertise in workshop management futurists, this workshop focuses on flores, founder of Action Technologies, and Mary Carriger for relieving me of hardware and software that enables puts it.

However, extended object-oriented oday, object-oriented programming important and powerful programming Italy, Sweden, the United languages and systems have paradigm, especially for Kingdom, and the United States were been developed that are adequate to the development of complex systems, invited to the workshop. This article handle AI applications. AI, raised and the major points made programming, a case of objectoriented however, is looking for knowledge during the presentations of the eight programming that has a representation and programming papers in the workshop's four sessions. AI, does not satisfy distributed AI applications and uses constructs (for The workshop started with an requirements because it lacks representation, example, frames) and notions (for introduction by Ibrahim in which he communication, and organization. Ibrahim posed a to the object-based concurrent The one-day workshop entitled number of questions related to the programming paradigm to close the Objects and AI, held in Sydney, Australia, theme of the workshop and asked gap with distributed AI, such as the on 25 August 1991 in conjunction the participants to address some of introduction of more powerful object with the 1991 International these questions during their talks and representations, a social theory of Joint Conference on Artificial Intelligence, discussion.

The Association for the Advancement of Artificial Intelligence sponsored a number of workshops in conjunction with the Eleventh National Conference on Artificial Intelligence held 11-15 July 1993 in Washington, D.C. This article contains reports of four of the workshops that were conducted: AI Models for System Engineering, Case-Based Reasoning, Reasoning about Function, and Validation and Verification of Knowledge Based Systems.

The overall goal is to reduce spacecraft weight.

The overall goal is to reduce spacecraft weight.

The methodological underpinnings of AI are slowly changing. Benchmarks, test beds, and controlled experimentation are becoming more common. Although we are optimistic that this change can solidify the science of AI, we also recognize a set of difficult issues concerning the appropriate use of this methodology. We discuss these issues as they relate to research on agent design. We survey existing test beds for agents and argue for appropriate caution in their use. We end with a debate on the proper role of experimental methodology in the design and validation of planning agents.