Efficient Algorithms to Solve Bayesian Stackelberg Games for Security Applications

Praveen Paruchuri; Jonathan P. Pearce; Janusz Marecki; Milind Tambe; Fernando Ordonez; Sarit Kraus

Efficient Algorithms to Solve Bayesian Stackelberg Games for Security Applications

Praveen Paruchuri, Jonathan P. Pearce, Janusz Marecki, Milind Tambe, Fernando Ordonez, Sarit Kraus

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

In a class of games known as Stackelberg games, one agent (the leader) must commit to a strategy that can be observed by the other agent (the adversary/follower) before the adversary chooses its own strategy. We consider Bayesian Stackelberg games, in which the leader is uncertain about the type of the adversary it may face. Such games are important in security domains, where, for example, a security agent (leader) must commit to a strategy of patrolling certain areas, and an adversary (follower) can observe this strategy over time before choosing where to attack. We present here two different MIP-formulations, ASAP (providing approximate policies with controlled randomization) and DOBSS (providing optimal policies) for Bayesian Stackelberg games. DOBSS is currently the fastest optimal procedure for Bayesian Stackelberg games and is in use by police at the Los Angeles International Airport(LAX) to schedule their activities.

Original language	English
Title of host publication	Proceedings of the 23rd AAAI Conference on Artificial Intelligence, AAAI 2008
Publisher	AAAI press
Pages	1559-1562
Number of pages	4
ISBN (Electronic)	9781577353683
State	Published - 2008
Event	23rd AAAI Conference on Artificial Intelligence, AAAI 2008 - Chicago, United States Duration: 13 Jul 2008 → 17 Jul 2008

Publication series

Name	Proceedings of the 23rd AAAI Conference on Artificial Intelligence, AAAI 2008

Conference

Conference	23rd AAAI Conference on Artificial Intelligence, AAAI 2008
Country/Territory	United States
City	Chicago
Period	13/07/08 → 17/07/08

Bibliographical note

Publisher Copyright:
Copyright © 2008, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Funding

Acknowledgements: This research is supported by the United States Department of Homeland Security through Center for Risk and Economic Analysis of Terrorism Events (CREATE). Sarit Kraus is also affiliated with UMIACS.

Funders	Funder number
U.S. Department of Homeland Security

Cite this

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title = "Efficient Algorithms to Solve Bayesian Stackelberg Games for Security Applications",

abstract = "In a class of games known as Stackelberg games, one agent (the leader) must commit to a strategy that can be observed by the other agent (the adversary/follower) before the adversary chooses its own strategy. We consider Bayesian Stackelberg games, in which the leader is uncertain about the type of the adversary it may face. Such games are important in security domains, where, for example, a security agent (leader) must commit to a strategy of patrolling certain areas, and an adversary (follower) can observe this strategy over time before choosing where to attack. We present here two different MIP-formulations, ASAP (providing approximate policies with controlled randomization) and DOBSS (providing optimal policies) for Bayesian Stackelberg games. DOBSS is currently the fastest optimal procedure for Bayesian Stackelberg games and is in use by police at the Los Angeles International Airport(LAX) to schedule their activities.",

author = "Praveen Paruchuri and Pearce, {Jonathan P.} and Janusz Marecki and Milind Tambe and Fernando Ordonez and Sarit Kraus",

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Paruchuri, P, Pearce, JP, Marecki, J, Tambe, M, Ordonez, F & Kraus, S 2008, Efficient Algorithms to Solve Bayesian Stackelberg Games for Security Applications. in Proceedings of the 23rd AAAI Conference on Artificial Intelligence, AAAI 2008. Proceedings of the 23rd AAAI Conference on Artificial Intelligence, AAAI 2008, AAAI press, pp. 1559-1562, 23rd AAAI Conference on Artificial Intelligence, AAAI 2008, Chicago, United States, 13/07/08.

Efficient Algorithms to Solve Bayesian Stackelberg Games for Security Applications. / Paruchuri, Praveen; Pearce, Jonathan P.; Marecki, Janusz et al.
Proceedings of the 23rd AAAI Conference on Artificial Intelligence, AAAI 2008. AAAI press, 2008. p. 1559-1562 (Proceedings of the 23rd AAAI Conference on Artificial Intelligence, AAAI 2008).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Efficient Algorithms to Solve Bayesian Stackelberg Games for Security Applications

Abstract

Publication series

Conference

Bibliographical note

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