Run the GAMUT: A comprehensive approach to evaluating game-theoretic algorithms

Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2004

Volumn 2, Issue , 2004, Pages 880-887

  Nudelman, Eugene ^a   Wortman, Jennifer ^a   Shoham, Yoav ^a   Leyton Brown, Kevin ^b  

^a STANFORD UNIVERSITY   (United States)

^b UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

GAME THEORETIC ALGORITHMS; GAME THEORETIC DOMAINS; NASH EQUILIBRIA;

BENCHMARKING; COMPUTATIONAL COMPLEXITY; INFORMATION SCIENCE; LEARNING ALGORITHMS; MULTI AGENT SYSTEMS; PROBABILISTIC LOGICS; PROBLEM SOLVING;

GAME THEORY;

EID: 4544335718     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. B. Blum, C. Shelton, and D. Koller. A continuation method for Nash equilibria in structured games. In IJCAI, 2003.
2. M. Bowling and M. Veloso. Rational and convergent learning in stochastic games. In IJCAI, 2001.
3. V. Conitzer and T. Sandholm. Complexity results about Nash equilibria. In IJCAI, 2003.
4. I. Gilboa and E. Zemel. Nash and correlated equilibria: Some complexity considerations. Games and Economic Behavior, 1, 1989.
5. S. Govindan and R. Wilson. A global Newton method to compute Nash equilibria. In J. of Economic Theory, 2003.
6. T. Grenager, R. Powers, and Y. Shoham. Dispersion games. In AAAI, 2002.
7. E. Kohlberg and J.F. Mertens. On the strategic stability of equilibria. Econometrica, 54, 1986.
8. C. Lemke and J. Howson, Equilibrium points of bimatrix games. Journal of the Society for Industrial and Applied Mathematics, 12, 1964.
9. K. Leyton-Brown, E. Nudelman, and Y. Shoham. Learning the empirical hardness of optimization problems: The case of combinatorial auctions. In CP, 2002.
10. M. L. Littman. Markov games as a framework for multiagent reinforcement learning. In ICML, 1994.
11. R. McKelvey and A. McLennan. Computation of equilibria in finite games. In H. Amman and D. Kendrick an J. Rust, editors, Handbook of Computational Economics, volume I. Elsevier, 1996.
12. R. D. McKelvey, A. McLennan, and T. Turocy. Gambit: game theory analysis software and tools, 1992. http://econweb.tamu.edu/gambit.
13. P. Milgrom and J. Roberts. Rationalizability, learning and equilibrium in games with strategic complementarities. Econometrica, 58, 1990.
14. C. Papadimitriou. Algorithms, games, and the internet. In STOC, 2001.
15. A. Rapoport, M. Guyer, and D. Gordon. The 2×2 Game. Univeristy of Michigan Press, 1976.
16. Y. Rinott and M. Scarsini. On the number of pure strategy Nash equilibria in random games. Games and Economic Behavior, 33, 2000.
17. R.W. Rosenthal. A class of games possessing pure-strategy Nash equilibria. International J. of Game Theory, 2, 1973.
18. W. H. Ruckle. Geometric Games and Their Applications. Pitman, 1983.
19. G. van der Laan, A. Talman, and L. van der Heyden. Simplicial variable dimension algorithms for solving the nonlinear complementarity problem on a product of unit simplices using a general labelling. Mathematics of Operations Research, 1987.
20. D. Vickrey and D. Koller. Multi-agent algorithms for solving graphical games. In AAAI, 2002.
21. B. von Stengel. Computing equilibria for two-person games. In R. Aumann and S. Hart, editors, Handbook of Game Theory, volume 3, chapter 45. North-Holland, 2002.
22. C. J. C. H. Watkins and P. Dayan. Technical note: Q-learning. Machine Learning, 8(3/4), May 1992.