Learning to compete, compromise, and cooperate in repeated general-sum games

ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning

Volumn , Issue , 2005, Pages 161-168

  Crandall, Jacob W ^a   Goodrich, Michael A ^a  

^a 3361 TMCB   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING ALGORITHMS; LEARNING SYSTEMS;

LEARNING AGENTS; MYOPIC APPROACHES; NASH EQUILIBRIUM (NE); REINFORCEMENT LEARNING ALGORITHMS;

GAME THEORY;

EID: 31844455339     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1102351.1102372     Document Type: Conference Paper

References (15)

1. Bowling, M. (2004). Convergence and no-regret in multiagent learning. Advances in Neural Information Processing Systems.
2. Bowling, M., & Veloso, M. (2002). Multiagent learning using a variable learning rate. Artificial Intelligence, 136(2), 215-250.
3. Crandall, J. W., & Goodrich, M. A. (2004). Learning near-pareto efficient solutions with minimal knowledge requirements using satisficing. AAAI Spring Symp. on Artificial Multiagent Learning.
4. Fudenberg, D., & Levine, D. K. (1998). The theory of learning in games. The MIT Press.
5. Gintis, H. (2000). Game theory evolving: A problem-centered introduction to modeling strategic behavior. Princeton, New Jersey: Princeton University Press.
6. th Intl. Conf., on Machine Learning.
7. th Intl. Conf. on Machine Learning.
8. th Intl. Conf. on Machine Learning.
9. th Intl. Conf. on Machine Learning.
10. Littman, M. L., & Stone, P. (2003). A polynomialtime nash equilibrium algorithm for repeated games. ACM Conf. on Electronic Commerce.
11. Nash, J. F. (1951). Non-cooperative games. Annals of Mathematics, 54, 286-295.
12. Sandholm, T. W., & Crites, R. H. (1995). Multiagent Reinforcement Learning in the Iterated Prisoner's Dilemma. Biosystems, Special Issue on the Prisoner's Dilemma.
13. th Intl. Joint Conf. on Artificial Intelligence.
14. Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction. The MIT Press.
15. Watkins, C. (1989). Learning from delayed rewards. Doctoral dissertation, University of Cambridge.