Dispersion games: General definitions and some specific learning results

Proceedings of the National Conference on Artificial Intelligence

Volumn , Issue , 2002, Pages 398-403

  Grenager, Trond ^a   Powers, Rob ^a   Shoham, Yoav ^a  

^a Stanford University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DYNAMIC PROGRAMMING; LEARNING SYSTEMS; MULTI AGENT SYSTEMS;

DISPERSION GAMES (DG);

GAME THEORY;

EID: 0036931075     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Alpern, S. 2001. Spatial dispersion as a dynamic coordination problem. Technical report, The London School of Economies.
2. Arthur, B. 1994. Inductive reasoning and bounded rationality. American Economic Association Papers 84:406-411.
3. Azar, Y.; Broder, A. Z.; Karlin, A. R.; and Upfal, E. 2000. Balanced allocations. SIAM Journal on Computing 29(1):180-200.
4. Balch, T. 1998. Behavioral diversity in learning robot teams.
5. Brafman, R. I., and Tennenholtz, M. 2000. A near-optimal polynomial time algorithm for learning in certain classes of stochastic games. Artificial Intelligence 121(1-2):31-47.
6. Brown, G. 1951. Iterative solution of games by fictitious play. In Activity Analysis of Production and Allocation. New York: John Wiley and Sons.
7. Challet, D., and Zhang, Y. 1997. Emergence of cooperation and organization in an evolutionary game. Physica A 246:407.
8. Claus, C., and Boutilier, C. 1998. The dynamics of reinforcement learning in cooperative multiagent systems. In AAAI/IAAI, 746-752.
9. Fudenberg, D., and Levine, D. K. 1998. The Theory of Learning in Games. Cambridge, MA: MIT Press.
10. Kaelbling, L. P.; Littman, M. L.; and Moore, A. P. 1996. Reinforcement learning: A survey. Journal of Artificial Intelligence Research 4:237-285.
11. Kalai, E., and Lehrer, E. 1993. Rational learning leads to nash equilibrium. Econometrica 61(5):1019-1045.
12. Littman, M. L. 1994. Markov games as a framework for multi-agent reinforcement learning. In Proceedings of the 11th International Conference on Machine Learning (ML-94), 157-163. New Brunswick, NJ: Morgan Kaufmann.
13. Osborne, M., and Rubinstein, A. 1994. A Course in Game Theory. Cambridge, Massachusetts: MIT Press.
14. Robinson, J. 1951. An iterative method of solving a game. Annals of Mathematics 54:298-301.
15. Schelling, T. 1960. The Strategy of Conflict. Cambridge, Massachusetts: Harvard University Press.