Efficient Strategy Computation in Zero-Sum Asymmetric Repeated Games

Handle URI:
http://hdl.handle.net/10754/626488
Title:
Efficient Strategy Computation in Zero-Sum Asymmetric Repeated Games
Authors:
Li, Lichun; Shamma, Jeff S. ( 0000-0001-5638-9551 )
Abstract:
Zero-sum asymmetric games model decision making scenarios involving two competing players who have different information about the game being played. A particular case is that of nested information, where one (informed) player has superior information over the other (uninformed) player. This paper considers the case of nested information in repeated zero-sum games and studies the computation of strategies for both the informed and uninformed players for finite-horizon and discounted infinite-horizon nested information games. For finite-horizon settings, we exploit that for both players, the security strategy, and also the opponent's corresponding best response depend only on the informed player's history of actions. Using this property, we refine the sequence form, and formulate an LP computation of player strategies that is linear in the size of the uninformed player's action set. For the infinite-horizon discounted game, we construct LP formulations to compute the approximated security strategies for both players, and provide a bound on the performance difference between the approximated security strategies and the security strategies. Finally, we illustrate the results on a network interdiction game between an informed system administrator and uniformed intruder.
KAUST Department:
Electrical Engineering Program
Publisher:
arXiv
Issue Date:
6-Mar-2017
ARXIV:
arXiv:1703.01952
Type:
Preprint
Additional Links:
http://arxiv.org/abs/1703.01952v2; http://arxiv.org/pdf/1703.01952v2
Appears in Collections:
Other/General Submission; Electrical Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorLi, Lichunen
dc.contributor.authorShamma, Jeff S.en
dc.date.accessioned2017-12-28T07:32:12Z-
dc.date.available2017-12-28T07:32:12Z-
dc.date.issued2017-03-06en
dc.identifier.urihttp://hdl.handle.net/10754/626488-
dc.description.abstractZero-sum asymmetric games model decision making scenarios involving two competing players who have different information about the game being played. A particular case is that of nested information, where one (informed) player has superior information over the other (uninformed) player. This paper considers the case of nested information in repeated zero-sum games and studies the computation of strategies for both the informed and uninformed players for finite-horizon and discounted infinite-horizon nested information games. For finite-horizon settings, we exploit that for both players, the security strategy, and also the opponent's corresponding best response depend only on the informed player's history of actions. Using this property, we refine the sequence form, and formulate an LP computation of player strategies that is linear in the size of the uninformed player's action set. For the infinite-horizon discounted game, we construct LP formulations to compute the approximated security strategies for both players, and provide a bound on the performance difference between the approximated security strategies and the security strategies. Finally, we illustrate the results on a network interdiction game between an informed system administrator and uniformed intruder.en
dc.publisherarXiven
dc.relation.urlhttp://arxiv.org/abs/1703.01952v2en
dc.relation.urlhttp://arxiv.org/pdf/1703.01952v2en
dc.rightsArchived with thanks to arXiven
dc.titleEfficient Strategy Computation in Zero-Sum Asymmetric Repeated Gamesen
dc.typePreprinten
dc.contributor.departmentElectrical Engineering Programen
dc.eprint.versionPre-printen
dc.contributor.institutionCoordinated Science Lab, University of Illinois at Urbana-Champaign, Urbana 61801, USA.en
dc.identifier.arxividarXiv:1703.01952en
kaust.authorShamma, Jeff S.en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.