Learning and approximating the optimal strategy to commit to

Conference Paper

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

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Duke Authors

Munagala, Kamesh

Cited Authors

Letchford, J; Conitzer, V; Munagala, K

Published Date

December 14, 2009

Published In

Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume / Issue

5814 LNCS /

Start / End Page

250 - 262

Electronic International Standard Serial Number (EISSN)

1611-3349

International Standard Serial Number (ISSN)

0302-9743

International Standard Book Number 10 (ISBN-10)

3642046444

International Standard Book Number 13 (ISBN-13)

9783642046445

Digital Object Identifier (DOI)

10.1007/978-3-642-04645-2_23

Citation Source

Scopus

Scholars@Duke