AAAI is pleased to present the elected fellows for 2000: Kenneth M. Ford, Eric Grimson, Leslie Pack Kaelbling, David Poole, Jonathan Schaeffer, and Bart Selman

The KDD conference series is the primary venue for presenting and publishing the highest quality advances in the field.

In 1998, the international planning community was invited to take part in the first planning competition, hosted by the Artificial Intelligence Planning Systems Conference, to provide a new impetus for empirical evaluation and direct comparison of automatic domain-independent planning systems. This article describes the systems that competed in the event, examines the results, and considers some of the implications for the future of the field.

We hope to see you in along with one or two new developments.

We order its events and find meaning in them by assimilating them to more or less familiar narratives. Temporal A wide variety of systems were presented: 1999, at the Sea Crest Oceanfront and modal logics have been used to story generation, interactive Resort and Conference Center. The reason about time, action, and adaptive fiction (including the first public titles of the five symposia were change and to program and verify demonstration from Joseph Bates's networked systems. How can we create characters from specifications of service quality in which interactive narrative emerges? The symposium focused mainly on a single, comprehensive theoretical framework, Clark's grounding model.

The 1998 Planning Competition at the AI Planning Systems Conference was the first of its kind. Its goal was to create planning domains that a wide variety of planning researchers could agree on to make comparison among planners more meaningful, measure overall progress in the field, and set up a framework for long-term creation of a repository of problems in a standard notation. A rules committee for the competition was created in 1997 and had long discussions on how the contest should go. One result of these discussions was the pddl notation for planning domains. This notation was used to set up a set of planning problems and get a modest problem repository started. As a result, five planning systems were able to compete when the contest took place in June 1998. All these systems solved problems in the strips framework, with some slight extensions. The attempt to find domains for other forms of planning foundered because of technical and organizational problems. In spite of this problem, the competition achieved its goals partially in that it con-firmed that substantial progress had occurred in some subfields of planning, and it allowed qualitative comparison among different planning algorithms. It is urged that the competition continue to take place and to evolve.

Intelligent-agent technology is one of the most exciting, active areas of research and development in computer science and information technology today. The First Asia-Pacific Conference on Intelligent- Agent Technology (IAT'99) attracted researchers and practitioners from diverse fields such as computer science, information systems, business, telecommunications, manufacturing, human factors, psychology, education, and robotics to examine the design principles and performance characteristics of various approaches in agent technologies and, hence, fostered the cross-fertilization of ideas on the development of autonomous agents and multiagent systems among different domains.

The 2000 Association of Computing Machinery Conference on Intelligent User Interfaces (IUI -- 2000) was held in New Orleans, Louisiana, from 9-12 January. This conference occupies the currently hot area that lies midway between the traditional fields of AI and computer-human interaction (CHI). For AI practitioners, this conference represents a good venue for learning about both how to design user interfaces for AI applications and how to use AI techniques to improve the user experience with more conventional applications. This year's conference drew the largest audience yet for an IUI conference, but the conference still remains at a manageable, single-track size. A wide range of high-quality presentations, tutorials, demonstrations, and invited speakers provided a bridge between the AI and CHI communities.

I report on my experience over the past few years in introducing automated, model-based diagnostic technologies into industrial settings. In partic-ular, I discuss the competition that this technology has been receiving from handcrafted, rule-based diagnostic systems that has set some high standards that must be met by model-based systems before they can be viewed as viable alternatives. The battle between model-based and rule-based approaches to diagnosis has been over in the academic literature for many years, but the situation is different in industry where rule-based systems are dominant and appear to be attractive given the considerations of efficiency, embeddability, and cost effectiveness. My goal in this article is to provide a perspective on this competition and discuss a diagnostic tool, called DTOOL/CNETS, that I have been developing over the years as I tried to address the major challenges posed by rule-based systems. In particular, I discuss three major features of the developed tool that were either adopted, designed, or innovated to address these challenges: (1) its compositional modeling approach, (2) its structure-based computational approach, and (3) its ability to synthesize embeddable diagnostic systems for a variety of software and hardware platforms.

This paper describes a novel method by which a spoken dialogue system can learn to choose an optimal dialogue strategy from its experience interacting with human users. The method is based on a combination of reinforcement learning and performance modeling of spoken dialogue systems. The reinforcement learning component applies Q-learning (Watkins, 1989), while the performance modeling component applies the PARADISE evaluation framework (Walker et al., 1997) to learn the performance function (reward) used in reinforcement learning. We illustrate the method with a spoken dialogue system named ELVIS (EmaiL Voice Interactive System), that supports access to email over the phone. We conduct a set of experiments for training an optimal dialogue strategy on a corpus of 219 dialogues in which human users interact with ELVIS over the phone. We then test that strategy on a corpus of 18 dialogues. We show that ELVIS can learn to optimize its strategy selection for agent initiative, for reading messages, and for summarizing email folders.