Strategy exploration in empirical games

  • Authors:

  • Patrick R. Jordan;L. Julian Schvartzman;Michael P. Wellman

  • Affiliations:

  • University of Michigan, Ann Arbor, Ml;University of Michigan, Ann Arbor, Ml;University of Michigan, Ann Arbor, Ml

  • Venue:
  • Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
  • Year:
  • 2010

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Abstract

Empirical analyses of complex games necessarily focus on a restricted set of strategies, and thus the value of empirical game models depends on effective methods for selectively exploring a space of strategies. We formulate an iterative framework for strategy exploration, and experimentally evaluate an array of generic exploration policies on three games: one infinite game with known analytic solution, and two relatively large empirical games generated by simulation. Policies based on iteratively finding a beneficial deviation or best response to the minimum-regret profile among previously explored strategies perform generally well on the profile-regret measure, although we find that some stochastic introduction of suboptimal responses can often lead to more effective exploration in early stages of the process. A novel formation-based policy performs well on all measures by producing low-regret approximate formations earlier than the deviation-based policies.