Conference articles PUBLISHED

1. Letchford, J; Conitzer, V; Munagala, K, Learning and approximating the optimal strategy to commit to, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5814 LNCS (December, 2009), pp. 250-262, Springer Berlin Heidelberg [doi].
   (last updated on 2024/04/18)

   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. © 2009 Springer-Verlag Berlin Heidelberg.