AI Seminar
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Tuesday, September 20th, 2005
4:00 pm - 5:30 pm
175 ATL (Large Conference Room)

"Empirical Game-Theoretic Analysis for Practical Strategic Reasoning"
Prof Michael Wellman
Artificial Intelligence Laboratory
University of Michigan

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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.