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.
Some of these results were briefly presented as part of a talk at the 2009 Bellairs Workshop on Algorithmic Game Theory. This work is funded by: Alfred P. Sloan Research Fellowships, NSF Grant IIS-0812113, NSF Career Award 0745761 and Grant CNS-0540347.
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Letchford, J., Conitzer, V., Munagala, K. (2009). Learning and Approximating the Optimal Strategy to Commit To. In: Mavronicolas, M., Papadopoulou, V.G. (eds) Algorithmic Game Theory. SAGT 2009. Lecture Notes in Computer Science, vol 5814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04645-2_23
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DOI: https://doi.org/10.1007/978-3-642-04645-2_23
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