NDLI: Safe opponent exploitation

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Safe opponent exploitation

Content Provider	ACM Digital Library
Author	Ganzfried, Samuel Sandholm, Tuomas
Abstract	We consider the problem of playing a finitely-repeated two-player zero-sum game safely---that is, guaranteeing at least the value of the game per period in expectation regardless of the strategy used by the opponent. Playing a stage-game equilibrium strategy at each time step clearly guarantees safety, and prior work has conjectured that it is impossible to simultaneously deviate from a stage-game equilibrium (in hope of exploiting a suboptimal opponent) and to guarantee safety. We show that such profitable deviations are indeed possible---specifically, in games where certain types of 'gift' strategies exist, which we define formally. We show that the set of strategies constituting such gifts can be strictly larger than the set of iteratively weakly-dominated strategies; this disproves another recent conjecture which states that all non-iteratively-weakly-dominated strategies are best responses to each equilibrium strategy of the other player. We present a full characterization of safe strategies, and develop efficient algorithms for exploiting suboptimal opponents while guaranteeing safety. We also provide analogous results for sequential perfect and imperfect-information games, and present safe exploitation algorithms and full characterizations of safe strategies for those settings as well. We present experimental results in Kuhn poker, a canonical test problem for game-theoretic algorithms. Our experiments show that 1) aggressive safe exploitation strategies significantly outperform adjusting the exploitation within equilibrium strategies and 2) all the safe exploitation strategies significantly outperform a (non-safe) best response strategy against strong dynamic opponents.
Starting Page	587
Ending Page	604
Page Count	18
File Format	PDF
ISBN	9781450314152
DOI	10.1145/2229012.2229056
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-06-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Multiagent learning Opponent exploitation Game theory
Content Type	Text
Resource Type	Article

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