Efficient algorithms to solve Bayesian Stackelberg games for security applications

Authors:
Praveen Paruchuri;Jonathan P. Pearce;Janusz Marecki;Milind Tambe;Fernando Ordonez;Sarit Kraus
Affiliations:
Intelligent Automation Inc., Rockville, MD;University of Southern California, Los Angeles, CA;University of Southern California, Los Angeles, CA;University of Southern California, Los Angeles, CA;University of Southern California, Los Angeles, CA;Bar-Ilan University, Israel
Venue:
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Year:
2008

Citing 7
Cited 4

Achieving network optima using Stackelberg routing strategies

IEEE/ACM Transactions on Networking (TON)
Computing the optimal strategy to commit to

EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Defending Critical Infrastructure

Interfaces
An efficient heuristic approach for security against multiple adversaries

Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Playing games for security: an efficient exact algorithm for solving Bayesian Stackelberg games

Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Deployed ARMOR protection: the application of a game theoretic model for security at the Los Angeles International Airport

Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: industrial track
Mixed-integer programming methods for finding Nash equilibria

AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2

Bayesian stackelberg games and their application for security at Los Angeles international airport

ACM SIGecom Exchanges
An efficient and accurate solution methodology for bilevel multi-objective programming problems using a hybrid evolutionary-local-search algorithm

Evolutionary Computation
Protecting location privacy: optimal strategy against localization attacks

Proceedings of the 2012 ACM conference on Computer and communications security
Equilibrium pricing with positive externalities

Theoretical Computer Science

Quantified Score

Hi-index	0.00

Visualization

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.