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.

It is a medium for disseminating information like to thank the Publications Committee for selecting me about AI areas and methods to readers across the entire as Editor, and especially to thank the Publications Committee field of AI, as well as to a broad multidisciplinary audience. Chair, Kenneth Ford, the previous Editor, Jude Shavlik, It is a journal of record for articles on important research and Publishing Consultant Mike Hamilton, for their warm and applications advances as well as for meeting reports, reviews, welcome, help, and very valuable guidance as I have embarked and discussions that illuminate the state of the art on the editorship. I am delighted to begin my term and emerging areas. Equally important, it is a forum for as Editor and look forward to working with the many who sharing visions for the field--perspectives on issues, priorities, contribute to AI Magazine's success. Indiana University helps to shape the future of artificial intelligence.

At the same time, we are witnessing a healthy increase in research activities on issues related to automated learning and discovery. Although the broad topic of automated change. The progressing computerization of learning and discovery is inherently professional and private life, paired with a cross-disciplinary in nature--it falls right into sharp increase in memory, processing, and networking the intersection of disciplines such as statistics, capabilities of today's computers, computer science, cognitive psychology, robotics, makes it increasingly possible to gather and and its users such as medicine, social sciences, analyze vast amounts of data. For the first time, and public policy--these fields have people all around the world are connected to mostly studied this topic in isolation. Where is each other electronically through the internet, the field, and where is it going?

Students interested in attending the National Conference on Artificial Intelligence in Austin, July 30-August 3, 2000, should consult the AAAI web site for further information about the Student Abstract program and the Doctoral Consortium. Details about these programs have also been mailed to all AAAI members. The Scholarship Program provides partial travel support and a complimentary technical program registration for students who (1) are full-time undergraduate or graduate students at colleges and universities; (2) are members of AAAI; (3) submit papers to the technical program or letters of recommendation from their faculty adviser; and (4) submit scholarship applications to AAAI by April 15, 2000. In addition, repeat scholarship applicants must have fulfilled the volunteer and reporting requirements for previous awards. In the event that scholarship applications AAAI President David Waltz presented The 1999 AAAI Classic Paper Award to exceed available funds, preference John McDermott for R1: An Expert in the Computer Systems Domain.

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.

Because perception-action systems are necessarily constrained by the physics of time and space, robotocists often assume they are best described using differential equations, a language that is specialized for describing the evolution of variables that represent physical quantities. However, when it comes to decision making, where the representations involved refer to goals, strategies, and preferences, AI offers a diverse range of formalisms to the modeler. However, the relationship between these two levels of representation -- signal and symbol -- are not well understood. If we are to achieve success in modeling intelligent physical agents, robotics and AI must reach a new consensus on how to integrate perception-action systems with systems designed for abstract reasoning.

Moravec's estimates of animal equivalence jostling of the atoms in a Moravec's strengths--his insightful are based solely on hardware rock can be seen as the operation of a complete, self-aware mind data analysis, extrapolation of technology complexity. It is often the case that (after Evert) (Everett, H., Many-to extreme conclusions, and hardware alone cannot deliver performance, Worlds of Interpretation/ Quantum provocative predictions--are all here but it also requires software Mechanics, Princeton University and will probably gain him some new sufficient to the task.

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. Since the idea was first mooted

The last few years have seen a surge in interest in the use of techniques from Bayesian decision theory to address problems in AI. Decision theory provides a normative framework for representing and reasoning about decision problems under uncertainty. Within the context of this framework, researchers in uncertainty in the AI community have been developing computational techniques for building rational agents and representations suited to engineering their knowledge bases. This special issue reviews recent research in Bayesian problem-solving techniques. The articles cover the topics of inference in Bayesian networks, decision-theoretic planning, and qualitative decision theory. Here, I provide a brief introduction to Bayesian networks and then cover applications of Bayesian problem-solving techniques, knowledge-based model construction and structured representations, and the learning of graphic probability models.

Confess your sins, O my sisters and brothers, points joyfully that really substantial progress in this that they shall be lifted from your shoulders. The sinner is working hard in some AI area. It believes that it the old list here. This formalism writes a paper saying so; but does nothing else. Kant, Hegel, Wittgenstein, Hume, Sarte,...) has also builds up around this particular formalism.