Austin, Texas, the "live music capital For more information about AAAI is pleased to announce the continued Conferences/conferences.html. Expository Writing Award will be presented members. AAAI is delighted to announce the 31-August 3 in Austin, Texas. This The conference will be held July collocation of SARA-2000 with AAAIaward joins the two special awards 31-August 3, 2000, at the Austin Convention 2000. The Symposium on Abstraction, established last year, the AAAI Classic Center and Hyatt Regency Reformulation, and Approximation Paper Award and the AAAI Distinguished Austin in Austin, Texas. AAAI-2000 will be held July 26-29, just outside Austin in Lago Vista on Lake Travis, Service Award. For more information about The AAAI Effective Expository Writing the Innovative Applications of SARA-2000, please visit sara2000.unl. Award honors the author(s) of a Artificial Intelligence, the Mobile edu/ high-quality, effective piece of writing, Robot Competition and Exhibition, AAAI also welcomes SARA-2000 as accessible to the general public or the Intelligent Systems Demonstrations, our first affiliate conference. For more to a broad AI audience (not just a subarea), the Robot Building Laboratory, information about the AAAI Affiliates written within the last two and the Doctoral Consortium. New Program, please write to Carol Hamilton years. The contribution should be for 2000 will be a technical paper at hamilton@aaai.org. Nominated papers must be Uncertainty: Operations Research AAAI is pleased to announce the continuation in English and must have been published Meets AI (Again)"; Justine Cassell, of its Student Abstract and in a publicly accessible place "Why Do We Need a Body Anyway?"; Poster Program, the SIGART/AAAI (for example, periodical, hard copy, or Carla Gomes, "Structure, Duality, and Doctoral Consortium, and the AAAI online journal but not only as a web Randomization: Common Themes in Scholarship and Volunteer Programs. The author(s) AI and OR"; James Hendler, "Missed Students interested in attending the of the award-winning paper(s) will Perceptions: AI versus the Funding National Conference on Artificial receive a $2500 prize (shared if more Agencies"; Geoff Hinton, "Modeling Intelligence in Austin, July 31-August than one author) as well as lodging High-Dimensional Data Distributions 3, 2000, should consult the AAAI web and travel to the National Conference by Combining Simple Experts"; Rich site for further information about all on Artificial Intelligence.

The goal of this research is to develop agents that are adaptive and predictable and timely. At first blush, these three requirements seem contradictory. For example, adaptation risks introducing undesirable side effects, thereby making agents' behavior less predictable. Furthermore, although formal verification can assist in ensuring behavioral predictability, it is known to be time-consuming. Our solution to the challenge of satisfying all three requirements is the following. Agents have finite-state automaton plans, which are adapted online via evolutionary learning (perturbation) operators. To ensure that critical behavioral constraints are always satisfied, agents' plans are first formally verified. They are then reverified after every adaptation. If reverification concludes that constraints are violated, the plans are repaired. The main objective of this paper is to improve the efficiency of reverification after learning, so that agents have a sufficiently rapid response time. We present two solutions: positive results that certain learning operators are a priori guaranteed to preserve useful classes of behavioral assurance constraints (which implies that no reverification is needed for these operators), and efficient incremental reverification algorithms for those learning operators that have negative a priori results.

Partially observable Markov decision processes (POMDPs) provide an elegant mathematical framework for modeling complex decision and planning problems in stochastic domains in which states of the system are observable only indirectly, via a set of imperfect or noisy observations. The modeling advantage of POMDPs, however, comes at a price -- exact methods for solving them are computationally very expensive and thus applicable in practice only to very simple problems. We focus on efficient approximation (heuristic) methods that attempt to alleviate the computational problem and trade off accuracy for speed. We have two objectives here. First, we survey various approximation methods, analyze their properties and relations and provide some new insights into their differences. Second, we present a number of new approximation methods and novel refinements of existing techniques. The theoretical results are supported by experiments on a problem from the agent navigation domain.

We investigate the space efficiency of a Propositional Knowledge Representation (PKR) formalism. Intuitively, the space efficiency of a formalism F in representing a certain piece of knowledge A, is the size of the shortest formula of F that represents A. In this paper we assume that knowledge is either a set of propositional interpretations (models) or a set of propositional formulae (theorems). We provide a formal way of talking about the relative ability of PKR formalisms to compactly represent a set of models or a set of theorems. We introduce two new compactness measures, the corresponding classes, and show that the relative space efficiency of a PKR formalism in representing models/theorems is directly related to such classes. In particular, we consider formalisms for nonmonotonic reasoning, such as circumscription and default logic, as well as belief revision operators and the stable model semantics for logic programs with negation. One interesting result is that formalisms with the same time complexity do not necessarily belong to the same space efficiency class.

Review of The Computational Beauty of Nature

AAAI 2000 Elected Fellows

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.

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. 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.

The Association for the Advancement of Artificial Intelligence, in cooperation, with Stanford University's Department of Com-puter Science, presented the 1999 Spring Symposium Series on 22 to 24 March 1999 at Stanford University. The titles of the seven symposia were (1) Agents with Adjustable Autonomy, (2) Artificial Intelligence and Computer Games, (3) Artificial Intelligence in Equipment Maintenance Service and Support, (4) Hybrid Systems and AI: Modeling, Analysis, and Control of Discrete Continuous Systems, (5) Intelligent Agents in Cyberspace, (6) Predictive Toxicology of Chemicals: Experiences and Impact of AI Tools, and (7) Search Techniques for Problem Solving under Uncertainty and Incomplete Information.