Learning and approximating the optimal strategy to commit to

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Letchford, J; Conitzer, V; Munagala, K

Published Date

  • December 14, 2009

Published In

Volume / Issue

  • 5814 LNCS /

Start / End Page

  • 250 - 262

Electronic International Standard Serial Number (EISSN)

  • 1611-3349

International Standard Serial Number (ISSN)

  • 0302-9743

Digital Object Identifier (DOI)

  • 10.1007/978-3-642-04645-2_23

Citation Source

  • Scopus