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.

Note 2. Operations Note 3. Investments at December 31, 1998 AAAI was formed in 1979 as a scientific Investments are stated at fair market value since it is readily determinable and the society, to encourage the basic knowledge investments are not necessarily being held to any maturity. Realized and unrealized of what constitutes intelligent thought gains and losses are reported in the statement of activities.

To coordinate, intelligent agents might need to know something about themselves, about each other, about how others view themselves and others, about how others think others view themselves and others, and so on. Taken to an extreme, the amount of knowledge an agent might possess to coordinate its interactions with others might outstrip the agent's limited reasoning capacity (its available time, memory, and so on). Much of the work in studying and building multiagent systems has thus been devoted to developing practical techniques for achieving coordination, typically by limiting the knowledge available to, or necessary for, agents. This article categorizes techniques for keeping agents suitably ignorant so that they can practically coordinate and gives a selective survey of examples of these techniques for illustration.

The conference will be held July 18-22, 1999, at the Omni Rosen Hotel and the Orange County Convention Center in Orlando, Florida. National Conference on Artificial by two keynote addresses: (1) AAAI is pleased to announce the Intelligence. This award will honor the author(s) of of AI in other organizations (for example, AAAI is happy to announce its sponsorship paper(s) deemed most influential, CRA, ACM, IEEE); or influential of the CHIKids program during chosen from a specific conference service as a government agency contract AAAI-99. The 1999 award will be given to monitor or program director, provides child care for conference the most influential paper(s) from the resulting in positive effects on the attendees' children, first started two First National Conference on Artificial field of AI. Nominees must be current years ago at the SIGCHI-96.

Victor Lesser (University of Massachusetts at Amherst) gave an invited talk on distributed interpretation and its of Hong Kong Polytechnic University, possible implication in DDM. Mining, brought interested researchers (Brigham Young University) and Salvatore The paper sessions ended with two and practitioners together and created Stolfo (Columbia University) working paper presentations by Billy an environment for crystallizing the presented the effects of class distribution Wallace and Juan Botia, Marcedes Garijo, fast-growing field of DDM. The concluding session was the panel Lawrence Hall, Nitesh Chawla, and 40 participants attended the workshop. Stolfo, George Cybenko Kevin W. Bowyer (all of University of The workshop had 13 presentations, Stolfo stressed suggested different techniques for Cybenko of Dartmouth University. Organizers sincerely hope that the session.

TACAIR-SOAR is an intelligent, rule-based system that generates believable humanlike behavior for large-scale, distributed military simulations. The innovation of the application is primarily a matter of scale and integration. The system is capable of executing most of the airborne missions that the U.S. military flies in fixed-wing aircraft. It accomplishes its missions by integrating a wide variety of intelligent capabilities, including real-time hierarchical execution of complex goals and plans, communication and coordination with humans and simulated entities, maintenance of situational awareness, and the ability to accept and respond to new orders while in flight. The system is currentl y deployed at the Oceana Naval Air Station WISSARD (what-if simulation system for advanced research and development) Lab and the Air Force Research Laboratory in Mesa, Arizona. Its most dramatic use was in the Synthetic Theater of War 1997, which was an operational training exercise that ran for 48 continuous hours during which TACAIR-SOAR flew all U.S. fixed-wing aircraft.

The Second International Symposium on Intelligent Data Analysis (IDA97) was held at Birkbeck College, University of London, on 4 to 6 August 1997. The main theme of IDA97 was to reason about how to analyze data,perhaps as human analysts do, by exploiting many methods from diverse disciplines. This article outlines several key issues and challenges, discusses how they were addressed at the conference, and presents opportunities for further work in the field.

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This article presents an experiment of expertise capitalization in road traffic-accident analysis. We study the integration of models of expertise from different members of an organization into a coherent corporate expertise model. We present our elicitation protocol and the generic models and tools we exploited for knowledge modeling in this context of multiple experts. We compare the knowledge models obtained for seven experts in accidentology and their representation through conceptual graphs. Finally, we discuss the results of our experiment from a knowledge capitalization viewpoint.