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''.

It is possible to argue, relatively convincingly, that any research topic only begins to become mature when it appears on a syllabus somewhere. Once the topic has become well enough understood that it can be explained easily to paying customers, and stable enough that anyone teaching it is not likely to have to update his/her teaching materials every few months as new developments are reported, it can be considered to have arrived. Another reasonable indicator of the maturity of a subject, a milestone along the road to academic respectability, is the publication of a really good book on the subject -- not another research monograph but a book that consolidates what is already known, surveys and relates existing ideas, and maybe even unifies some of them. Grigoris Antoniou's Nonmonotonic Reasoning is just such a milestone -- well written, informative, and a good source of information on an important and complex subject.

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, held the 1998 Spring Symposium Series on 23 to 25 March at Stanford University. The topics of the eight symposia were (1) Applying Machine Learning to Discourse Processing, (2) Integrating Robotic Research: Taking the Next Leap, (3) Intelligent Environments, (4) Intelligent Text Summarization, (5) Interactive and Mixed-Initiative Decision-Theoretic Systems, (6) Multimodal Reasoning, (7) Prospects for a Common-Sense Theory of Causation, and (8) Satisficing Models.

This article summarizes the Conference on Automated Learning and Discovery (CONALD), which took place in June 1998 at Carnegie Mellon University. CONALD brought together an interdisciplinary group of scientists concerned with decision making based on data. One of the meeting's focal points was the identification of promising research topics, which are discussed toward the end of this article.

The Association for the Advancement of Artificial Intelligence (AAAI) held its 1998 Fall Symposium Series on 23 to 25 October at the Omni Rosen Hotel in Orlando, Florida. This article contains summaries of seven of the symposia that were conducted: (1) Cognitive Robotics; (2) Distributed, Continual Planning; (3) Emotional and Intelligent: The Tangled Knot of Cognition; (4) Integrated Planning for Autonomous Agent Architectures; (5) Planning with Partially Observable Markov Decision Processes; (6) Reasoning with Visual and Diagrammatic Representations; and (7) Robotics and Biology: Developing Connections.

The problem of learning is arguably at the very core of the problem of intelligence, both biological and artificial. In this article, we review our work over the last 10 years in the area of supervised learning, focusing on three interlinked directions of research -- (1) theory, (2) engineering applications (making intelligent software), and (3) neuroscience (understanding the brain's mechanisms of learnings) -- that contribute to and complement each other.

Human intelligence is shaped by what is most important to us -- the things that cause ecstasy, despair, pleasure, pain, and other intense emotions. The ability to separate the important from the unimportant underlies such faculties as attention, focusing, situation and outcome assessment, priority setting, judgment, taste, goal selection, credit assignment, the selection of relevant memories and precedents, and learning from experience. AI has for the most part focused on logic and reasoning in artificial situations where only relevant variables and operators are specified and has paid insufficient attention to processes of reducing the richness and disorganization of the real world to a form where logical reasoning can be applied. This article discusses the role of importance judgment in intelligence; provides some examples of research that make use of importance judgments; and offers suggestions for new mechanisms, architectures, applications, and research directions for AI.

AI has embraced medical applications from its inception, and some of the earliest work in successful application of AI technology occurred in medical contexts. Medicine in the twenty-first century will be very different than medicine in the late twentieth century. Fortunately, the technical challenges to AI that emerge are similar, and the prospects for success are high.

The figure shows an ideal continuous loop from theory to feasibility understanding the problem of intelligence. In reality, the learning is also becoming a key to the study of interactions--as one might expect--are less artificial and biological vision. For example in years, both computer vision--which attempts 1990, ideas from the mathematics of learning to build machines that see--and visual neuroscience--which theory--radial basis function networks--suggested aims to understand how our a model for biological object recognition visual system works--are undergoing a fundamental that led to the physiological experiments change in their approaches. Visual neuroscience in cortex described later in the article. It was is beginning to focus on the mechanisms only later that the same idea found its way into that allow the cortex to adapt its the computer graphics applications described circuitry and learn a new task. In this article, we concentrate on one aspect of Vision systems that learn and adapt represent learning: supervised learning.

The Association for the Advancement of Artificial Intelligence (AAAI) held its 1998 Fall Symposium Series on 23 to 25 October at the Omni Rosen Hotel in Orlando, Florida. This article contains summaries of seven of the symposia that were conducted: (1) Cognitive Robotics; (2) Distributed, Continual Planning; (3) Emotional and Intelligent: The Tangled Knot of Cognition; (4) Integrated Planning for Autonomous Agent Architectures; (5) Planning with Partially Observable Markov Decision Processes; (6) Reasoning with Visual and Diagrammatic Representations; and (7) Robotics and Biology: Developing Connections.