Praveen Paruchuri
ABSTRACT
In adversarial multiagent domains, security, commonly defined as the ability to deal with intentional threats from other agents, is a critical issue. This paper focuses on domains where these threats come from unknown adversaries. These domains can be modeled as Bayesian games; much work has been done on finding equilibria for such games. However, it is often the case in multiagent security domains that one agent can commit to a mixed strategy which its adversaries observe before choosing their own strategies. In this case, the agent can maximize reward by finding an optimal strategy, without requiring equilibrium. Previous work has shown this problem of optimal strategy selection to be NP-hard. Therefore, we present a heuristic called ASAP, with three key advantages to address the problem. First, ASAP searches for the highest-reward strategy, rather than a Bayes-Nash equilibrium, allowing it to find feasible strategies that exploit the natural first-mover advantage of the game. Second, it provides strategies which are simple to understand, represent, and implement. Third, it operates directly on the compact, Bayesian game representation, without requiring conversion to normal form. We provide an efficient Mixed Integer Linear Program (MILP) implementation for ASAP, along with experimental results illustrating significant speedups and higher rewards over other approaches.
- R. W. Beard and T. McLain. Multiple UAV cooperative search under collision avoidance and limited range communication constraints. In IEEE CDC, 2003.Google Scholar
Cross Ref
- D. Bertsimas and J. Tsitsiklis. Introduction to Linear Optimization. Athena Scientific, 1997. Google ScholarDigital Library
- J. Brynielsson and S. Arnborg. Bayesian games for threat prediction and situation analysis. In FUSION, 2004.Google Scholar
- Y. Chevaleyre. Theoretical analysis of multiagent patrolling problem. In AAMAS, 2004. Google ScholarDigital Library
- V. Conitzer and T. Sandholm. Choosing the best strategy to commit to. In ACM Conference on Electronic Commerce, 2006. Google ScholarDigital Library
- D. Fudenberg and J. Tirole. Game Theory. MIT Press, 1991.Google Scholar
- C. Gui and P. Mohapatra. Virtual patrol: A new power conservation design for surveillance using sensor networks. In IPSN, 2005. Google ScholarDigital Library
- J. C. Harsanyi and R. Selten. A generalized Nash solution for two-person bargaining games with incomplete information. Management Science, 18(5):80--106, 1972.Google ScholarDigital Library
- D. Koller and A. Pfeffer. Generating and solving imperfect information games. In IJCAI, pages 1185--1193, 1995. Google ScholarDigital Library
- D. Koller and A. Pfeffer. Representations and solutions for game-theoretic problems. Artificial Intelligence, 94(1):167--215, 1997. Google ScholarDigital Library
- C. Lemke and J. Howson. Equilibrium points of bimatrix games. Journal of the Society for Industrial and Applied Mathematics, 12:413--423, 1964.Google ScholarCross Ref
- R. J. Lipton, E. Markakis, and A. Mehta. Playing large games using simple strategies. In ACM Conference on Electronic Commerce, 2003. Google ScholarDigital Library
- A. Machado, G. Ramalho, J. D. Zucker, and A. Drougoul. multiagent patrolling: an empirical analysis on alternative architectures. In MABS, 2002. Google ScholarDigital Library
- P. Paruchuri, M. Tambe, F. Ordonez, and S. Kraus. Security in multiagent systems by policy randomization. In AAMAS, 2006. Google ScholarDigital Library
- T. Roughgarden. Stackelberg scheduling strategies. In ACM Symposium on TOC, 2001. Google ScholarDigital Library
- S. Ruan, C. Meirina, F. Yu, K. R. Pattipati, and R. L. Popp. Patrolling in a stochastic environment. In 10th Intl. Command and Control Research Symp., 2005.Google Scholar
- T. Sandholm, A. Gilpin, and V. Conitzer. Mixed-integer programming methods for finding nash equilibria. In AAAI, 2005. Google ScholarDigital Library
- S. Singh, V. Soni, and M. Wellman. Computing approximate Bayes-Nash equilibria with tree-games of incomplete information. In ACM Conference on Electronic Commerce, 2004. Google ScholarDigital Library
Index Terms
- An efficient heuristic approach for security against multiple adversaries
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