Quick polytope approximation of all correlated equilibria in stochastic games

Liam MacDermed, Karthik S. Narayan, Charles L. Isbell, Lora G. Weiss

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Stochastic or Markov games serve as reasonable models for a variety of domains from biology to computer security, and are appealing due to their versatility. In this paper we address the problem of finding the complete set of correlated equilibria for general-sum stochastic games with perfect information. We present QPACE - an algorithm orders of magnitude more efficient than previous approaches while maintaining a guarantee of convergence and bounded error. Finally, we validate our claims and demonstrate the limits of our algorithm with extensive empirical tests.

Original languageEnglish (US)
Title of host publicationAAAI-11 / IAAI-11 - Proceedings of the 25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference
Pages707-712
Number of pages6
StatePublished - Nov 2 2011
Event25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11 - San Francisco, CA, United States
Duration: Aug 7 2011Aug 11 2011

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume1

Other

Other25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11
CountryUnited States
CitySan Francisco, CA
Period8/7/118/11/11

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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  • Cite this

    MacDermed, L., Narayan, K. S., Isbell, C. L., & Weiss, L. G. (2011). Quick polytope approximation of all correlated equilibria in stochastic games. In AAAI-11 / IAAI-11 - Proceedings of the 25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference (pp. 707-712). (Proceedings of the National Conference on Artificial Intelligence; Vol. 1).