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AI Seminar

Empirical Game-Theoretic Analysis for Practical Strategic Reasoning

Michael Wellman

The games agents play–in markets, conflicts, or most other contexts–often defy strict game-theoretic analysis. Games may be unmanageably large (combinatorial or infinite state or action spaces), and present severely imperfect information, which could be further complicated by partial dynamic revelation. Moreover, the game may not even be specified in the precise form required for game-theoretic reasoning. For example, we may have at best a simulator or limited access to the real world for experimentation, or some other form of experiential data or knowledge.
With colleagues and students over the past few years, I have been developing a body of techniques for strategic analysis, adopting the game-theoretic framework but employing it in domains where direct "model-and-solve" cannot apply. This empirical game-theoretic methodology embraces simulation, approximation, statistics and learning, and search. Through examples of such techniques and illustrative application to auction games, supply chains, 4-player chess, and other scenarios, I argue that such a toolkit can support practical automation of routine strategic reasoning.
Michael Wellman received a PhD from the Massachusetts Institute of Technology in 1988 for his work in qualitative probabilistic reasoning and decision-theoretic planning. From 1988 to 1992, Wellman conducted research in these areas at the USAF's Wright Laboratory. For the past dozen+ years, his research has focused on computational market mechanisms for distributed decision making and electronic commerce. As Chief Market Technologist for TradingDynamics, Inc. (now part of Ariba), he designed configurable auction technology for dynamic business-to-business commerce. Wellman is Chair of the ACM Special Interest Group on Electronic Commerce (SIGecom), and previously served as Executive Editor of the Journal of Artificial Intelligence Research. He has been elected Councilor and Fellow of the American Association for Artificial Intelligence.

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