MP Wellman, DM Reeves, KM Lochner, S-F Cheng, and R Suri

Twentieth National Conference on Artificial Intelligence, 2005.
Copyright (c) 2005, American Association for Artificial Intelligence


To deal with exponential growth in the size of a game with the number of agents, we propose an approximation based on a hierarchy of reduced games. The reduced game achieves savings by restricting the number of agents playing any strategy to fixed multiples. We validate the idea through experiments on randomly generated local-effect games. An extended application to strategic reasoning about a complex trading scenario motivates the approach, and demonstrates methods for game-theoretic reasoning over incompletely-specified games at multiple levels of granularity.


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