Learning and Approximating the Optimal Strategy to Commit To

Authors:
Joshua Letchford;Vincent Conitzer;Kamesh Munagala
Affiliations:
Department of Computer Science, Duke University, Durham, USA;Department of Computer Science, Duke University, Durham, USA;Department of Computer Science, Duke University, Durham, USA
Venue:
SAGT '09 Proceedings of the 2nd International Symposium on Algorithmic Game Theory
Year:
2009

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Abstract

Computing optimal Stackelberg strategies in general two-player Bayesian games (not to be confused with Stackelberg strategies in routing games) is a topic that has recently been gaining attention, due to their application in various security and law enforcement scenarios. Earlier results consider the computation of optimal Stackelberg strategies, given that all the payoffs and the prior distribution over types are known. We extend these results in two different ways. First, we consider learning optimal Stackelberg strategies. Our results here are mostly positive. Second, we consider computing approximately optimal Stackelberg strategies. Our results here are mostly negative.