Learning and approximating the optimal strategy to commit to
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Letchford, J; Conitzer, V; Munagala, K
Published in: Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
December 14, 2009
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.
Duke Scholars
Published In
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
DOI
EISSN
1611-3349
ISSN
0302-9743
Publication Date
December 14, 2009
Volume
5814 LNCS
Start / End Page
250 / 262
Related Subject Headings
- Artificial Intelligence & Image Processing
- 46 Information and computing sciences
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MLA
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Letchford, J., Conitzer, V., & Munagala, K. (2009). Learning and approximating the optimal strategy to commit to. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics (Vol. 5814 LNCS, pp. 250–262). https://doi.org/10.1007/978-3-642-04645-2_23
Letchford, J., V. Conitzer, and K. Munagala. “Learning and approximating the optimal strategy to commit to.” In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 5814 LNCS:250–62, 2009. https://doi.org/10.1007/978-3-642-04645-2_23.
Letchford J, Conitzer V, Munagala K. Learning and approximating the optimal strategy to commit to. In: Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics. 2009. p. 250–62.
Letchford, J., et al. “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, 2009, pp. 250–62. Scopus, doi:10.1007/978-3-642-04645-2_23.
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. 2009. p. 250–262.
Published In
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
DOI
EISSN
1611-3349
ISSN
0302-9743
Publication Date
December 14, 2009
Volume
5814 LNCS
Start / End Page
250 / 262
Related Subject Headings
- Artificial Intelligence & Image Processing
- 46 Information and computing sciences