First, if a team's collaboration is better than the other team, some of the statistical Score books are useful for analyzing teammate. FALL 2000 25 national conference on artificial intelligence innovative applications of artificial intelligence spring symposium series fall symposium series aaai press ai mgazine member's electronic library aaai fellows classic paper award distinguished service award allen newell award mobile robot competition robot exhibition botball tournament discounts on ai books aaai intel science and engineering awards effective expository writing award ai topics website discounts on journals scientific policy grants for workshops, conferences, and symposia technical reports electronic directory of ai scientists

RoboCup is an initiative designed to promote the full integration of AI and robotics research. Following the success of the first RoboCup in 1997 at Nagoya (Kitano 1998; Noda et al. 1998) and the second RoboCup in Paris in 1998, the Third Robot World Cup Soccer Games and Conferences, RoboCup-99, were held in Stockholm from 27 July to 4 August 1999 in conjunction with the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI-99). There were four different leagues: (1) the simulation league, (2) the small-size real robot league, (3) the middle-size real robot league, and (4) the Sony legged robot league. RoboCup-2000, the Fourth Robot World Cup Soccer Games and Conferences, will take place in Melbourne, Australia, in August 2000.

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.