Fourth, causality is still an important issue; some formal models of causality have surprisingly close connections to standard nonmonotonic techniques. Fifth, the nonmonotonic logics being used most widely are the classical ones: default logic, circumscription, and by Isaac Levi; (3) Nonmonotonic Reasoning autoepistemic logic. Maybe the most remarkable trend he Seventh International Workshop was held in Trento, Italy, Tolerance by John McCarthy; (4) that became apparent during the on 30 May to 1 June 1998 in conjunction Learning to Make Nonmonotonic workshop was the new excitement with the Sixth International Inferences by Dan Roth; and (5) From among the participants. The depression Conference on the Principles of Features and Fluents to Thinking that plagued a number of people Knowledge Representation and Reasoning When Flying--Reasoning about in the field seems to be over. The workshop was Actions in an Intelligent UAV by Erik common feeling was that the theory sponsored by the American Association Sandewall.

Researchers and technology developers from the National Aeronautics and Space Administration (NASA), other government agencies, academia, and industry recently met in Pasadena, California, to take stock of past and current work and future challenges in the application of AI to highly autonomous systems. The meeting was catalyzed by new opportunities in developing autonomous spacecraft for NASA and was in part a celebration of the fictional birth year of the HAL-9000 computer.

The Eleventh International Workshop on Qualitative Reasoning was held in Cortona, Italy, on 3 to 6 June 1997. Participants included scientists from both qualitative reasoning and quantitative mathematical modeling communities. This article summarizes the significant issues and discussion raised during the workshop.

Humans are social creatures. Much of our intelligence derives from our ability to manipulate our environment through collaborative endeavors. Most extant computer programs and interfaces do little to take advantage of such manifestly human talents and interests, leaving broad avenues of human-computer communication unexplored. Although it is still considered controversial, there are many who believe the harnessing of social communication to be rich in possibilities for modern software. In this article, we look at a number of autonomous agent systems that embody their intelligence at least partially through the projection of a believable, engaging, synthetic persona. Among other topics, we touch briefly on samples of research that explore synthetic personality, representations of emotion, societies of fanciful and playful characters, intelligent and engaging automated tutors, and users projected as avatars into virtual worlds.

In the last few years, we have witnessed a major growth in the use of empirical methods in AI. In part, this growth has arisen from the availability of fast networked computers that allow certain problems of a practical size to be tackled for the first time. There is also a growing realization that results obtained empirically are no less valuable than theoretical results. Experiments can, for example, offer solutions to problems that have defeated a theoretical attack and provide insights that are not possible from a purely theoretical analysis. I identify some of the emerging trends in this area by describing a recent workshop that brought together researchers using empirical methods as far apart as robotics and knowledge-based systems.

Tom Mitchell states that the goal of his text Machine Learning is to present the key algorithms and theory that form the core of machine learning. Not only has Mitchell suc-ceeded in his primary goal, but he has accomplished a number of other important goals.

However, all eligible students are Intelligence (AAAI-98) will be Third Annual Genetic Programming encouraged to apply. After the conference, available in late March by writing to Conference (GP-98), July 22-25 an expense report will be required ncai@aaai.org Please note that the deadline Eleventh Annual Conference on scholarships@aaai.org or at 445 Burgess for early registrations is May 27, 1998. Computational Learning Theory Drive, Menlo Park, CA 94025, The conference will be held July (COLT '98), July 24-26 (theory.lcs.mit. All student scholarship recipients Monona Terrace Convention Center, Fifteenth International Conference will be required to participate in the designed by Frank Lloyd Wright, in on Machine Learning (ICML '98), July Student Volunteer Program to support Madison, Wisconsin.

An adaptive online algorithm extending the learning of learning idea is proposed and theoretically motivated. Relying only on gradient flowinformation it can be applied to learning continuous functions or distributions, even when no explicit loss function is given andthe Hessian is not available. Its efficiency is demonstrated for a non-stationary blind separation task of acoustic signals. 1 Introduction Neural networks provide powerful tools to capture the structure in data by learning. Often the batch learning paradigm is assumed, where the learner is given all training examplessimultaneously and allowed to use them as often as desired. In large practical applications batch learning is often experienced to be rather infeasible and instead online learning is employed.

An adaptive online algorithm extending the learning of learning idea is proposed and theoretically motivated. Relying only on gradient flow information it can be applied to learning continuous functions or distributions, even when no explicit loss function is given and the Hessian is not available. Its efficiency is demonstrated for a non-stationary blind separation task of acoustic signals.

In recent years, there has been a resurgence in research on empirical methods in natural language processing. These methods employ learning techniques to automatically extract linguistic knowledge from natural language corpora rather than require the system developer to manually encode the requisite knowledge. The current special issue reviews recent research in empirical methods in speech recognition, syntactic parsing, semantic processing, information extraction, and machine translation. This article presents an introduction to the series of specialized articles on these topics and attempts to describe and explain the growing interest in using learning methods to aid the development of natural language processing systems.