Double-oracle algorithm for computing an exact nash equilibrium in zero-sum extensive-form games

12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013

Volumn 1, Issue , 2013, Pages 335-342

  Bošanský, Branislav ^a   Kiekintveld, Christopher ^b   Lisý, Viliam ^a   Čermák, Jiří ^a   Pěchouček, Michal ^a  

^a CZECH TECHNICAL UNIVERSITY IN PRAGUE   (Czech Republic)

^b University of Texas at El Paso   (United States)

Author keywords

Algorithms; Exact Nash equilibrium; Extensive form games; Game theory

Indexed keywords

ALGORITHMS; AUTONOMOUS AGENTS; GAME THEORY; ITERATIVE METHODS; MULTI AGENT SYSTEMS;

ALGORITHMIC FRAMEWORK; EXACT SOLUTION; EXTENSIVE-FORM GAMES; IMPERFECT INFORMATION; ITERATIVE ALGORITHM; LINEAR PROGRAMS; NASH EQUILIBRIA; THEORETICAL GUARANTEES;

COMPUTER GAMES;

EID: 84899413774     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. B. Bosansky, C. Kiekintveld, V. Lisy, and M. Pechoucek. Iterative Algorithm for Solving Two-player Zero-sum Extensive-form Games with Imperfect Information. In Proc. of European Conf. on Artificial Intelligence (ECAI), 2012.
2. S. Hoda, A. Gilpin, J. Peña, and T. Sandholm. Smoothing techniques for computing nash equilibria of sequential games. Math. Oper. Res., 35(2): 494-512, May 2010.
3. M. Jain, D. Korzhyk, O. Vanek, V. Conitzer, M. Tambe, and M. Pechoucek. Double Oracle Algorithm for Zero-Sum Security Games on Graph. In Proc. of the Int. Conf on Autonomous Agents and Multiagent Systems (AAMAS), 2011.
4. M. Johanson, M. Bowling, K. Waugh, and M. Zinkevich. Accelerating best response calculation in large extensive games. In Proc. of Int. Joint Conf. on Artificial Intelligence (IJCAI), 2011.
5. D. Koller, N. Megiddo, and B. von Stengel. Efficient computation of equilibria for extensive two-person games. Games and Economic Behavior, 14(2), 1996.
6. M. Lanctot, K. Waugh, M. Zinkevich, and M. Bowling. Monte carlo sampling for regret minimization in extensive games. In Advances in Neural Information Processing Syst. (NIPS), pages 1078-1086, 2009.
7. H. B. McMahan and G. J. Gordon. A fast bundle-based anytime algorithm for poker and other convex games. Journal of Machine Learning Research - Proceedings Track, 2: 323-330, 2007.
8. H. B. McMahan, G. J. Gordon, and A. Blum. Planning in the presence of cost functions controlled by an adversary. In Int. Conf. on Machine Learning, pages 536-543, 2003.
9. M. J. V. Ponsen, S. de Jong, and M. Lanctot. Computing approximate nash equilibria and robust best-responses using sampling. J. Artif. Intell. Res. (JAIR), 42: 575-605, 2011.
10. Y. Shoham and K. Leyton-Brown. Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. 2009.
11. M. Tambe. Security and Game Theory: Algorithms, Deployed Systems, Lessons Learned. Cambridge University Press, 2011.
12. B. von Stengel. Efficient computation of behavior strategies. Games and Economic Behavior, 14: 220-246, 1996.
13. K. Waugh, N. Bard, and M. Bowling. Strategy grafting in extensive games. In Advances in Neural Information Processing Systems 22 (NIPS), pages 2026-2034, 2009.
14. M. P. Wellman. Trading Agents. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Pub., 2011.
15. M. Zinkevich, M. Bowling, and N. Burch. A new algorithm for generating equilibria in massive zero-sum games. In Proc. of National Conference on Artificial Intelligence (AAAI), pages 788-793, 2007.
16. M. Zinkevich, M. Johanson, M. Bowling, and C. Piccione. Regret minimization in games with incomplete information. Advances in Neural Information Processing Syst. (NIPS), 20: 1729-1736, 2008.