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Article
Approximate strategic reasoning through hierarchical reduction of large symmetric games
Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI-05): July 9-13, 2005, Pittsburgh, PA
  • Michael P. WELLMAN, University of Michigan
  • Daniel M. REEVES
  • Kevin M. LOCHNER
  • Shih-Fen CHENG, Singapore Management University
  • Rahul SURI
Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2005
Abstract

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.

ISBN
9781577352365
Publisher
AAAI Press
City or Country
Menlo Park, CA
Creative Commons License
Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International
Additional URL
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.368.6230
Citation Information
Michael P. WELLMAN, Daniel M. REEVES, Kevin M. LOCHNER, Shih-Fen CHENG, et al.. "Approximate strategic reasoning through hierarchical reduction of large symmetric games" Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI-05): July 9-13, 2005, Pittsburgh, PA (2005) p. 502 - 508
Available at: http://works.bepress.com/sfcheng/12/