History-dependent graphical multiagent models

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

Volumn 3, Issue , 2010, Pages 1215-1222

  Duong, Quang ^a   Wellman, Michael P ^a   Singh, Satinder ^b   Vorobeychik, Yevgeniy ^b  

^a UNIVERSITY OF MICHIGAN   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Agent based modeling; Graphical multiagent models; Multiagent reasoning

Indexed keywords

AUTONOMOUS AGENTS; COMPUTATIONAL METHODS; PROBABILITY DISTRIBUTIONS;

AGENT-BASED MODEL; BEHAVIOUR MODELS; DYNAMICS MODELS; GRAPHICAL MULTIAGENT MODEL; INDIVIDUAL BEHAVIOR; JOINT BEHAVIOR; MULTI-AGENT MODEL; MULTI-AGENT REASONINGS; PROBABILITY: DISTRIBUTIONS; SYSTEM BEHAVIORS;

MULTI AGENT SYSTEMS;

EID: 84856147305     PISSN: 15488403     EISSN: 15582914     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. J. Y. Broadway, J. S. Yedidia, W. T. Freeman, and Y. Weiss. Generalized belief propagation. In Thirteenth Annual Conference on Advances in Neural Information Processing Systems, pages 689-695, Denver, 2000.
2. C. Camerer and T. Ho. Experienced-weighted attraction learning in normal form games. Econometrica, 67(4):827-874, 1999.
3. C. Daskalakis and C. H. Papadimitriou. Computing pure Nash equilibria in graphical games via Markov random fields. In Seventh ACM conference on Electronic Commerce, pages 91-99, Ann Arbor, MI, 2006.
4. Q. Duong, M. P. Wellman, and S. Singh. Knowledge combination in graphical multiagent models. In Twenty-Fourth Conference on Uncertainty in Artificial Intelligence, pages 153-160, Helsinki, 2008.
5. Q. Duong, Y. Vorobeychik, S. Singh, and M. P. Wellman. Learning graphical game models. In Twenty-First International Joint Conference on Artificial Intelligence, pages 116-121, Pasadena, CA, 2009.
6. D. Fudenberg and D. K. Levine. The Theory of Learning in Games. MIT Press, 1998.
7. Y. Gal and A. Pfeffer. Networks of influence diagrams: A formalism for representing agents' beliefs and decisionmaking processes. Journal of Artificial Intelligence Research, 33:109-147, 2008.
8. M. Granovetter. Threshold models of collective behavior. American Journal of Sociology, 83(6). T420-43, 1978.
9. D. Hennes, K. Tuyls, and M. Rauterberg. State-coupled replicator dynamics. In Eighth International Conference on Autonomous Agents and Multi-Agent Systems, pages 789-796, Budapest, 2009.
10. A. X. Jiang, K. Leyton-Brown, and N. A. R. Bhat. Action-graph games. Technical Report UBC CS TR-2008-13, University of British Columbia, 2008.
11. P. R. Jordan and M. P. Wellman. Designing an ad auctions game for the trading agent competition. In Agent-Mediated Electronic Commerce: Designing Trading Strategies and Mechanisms for Electronic Markets, Lecture Notes in Business Information Processing. Springer-Verlag, 2010.
12. M. Kearns and J. Tan. Biased voting and the Democratic primary problem. In Fourth International Workshop on Internet and Network Economics, pages 639-652, Shanghai, 2008.
13. M. Kearns and J. Wortman. Learning from collective behavior. In Twenty-First Annual Conference on Learning Theory, pages 99-110, Helsinki, 2008.
14. M. Kearns, M. L. Littman, and S. Singh. Graphical models for game theory. In Seventeenth Conference on Uncertainty in Artificial Intelligence, pages 253-260, Seattle, 2001.
15. M. Kearns, S. Judd, J. Tan, and J. Wortman. Behavioral experiments on biased voting in networks. Proceedings of the National Academy of Sciences, 106(5):1347-52, 2009.
16. J. Kleinberg. Cascading behavior in networks: Algorithmic and economic issues. In N. Nisan, T. Roughgarden, E. Tardos, and V. V. Vazirani, editors, Algorithmic Game Theory, pages 613-632. Cambridge University Press, 2007.
17. D. Koller and B. Milch. Multi-agent influence diagrams for representing and solving games. In Seventeenth International Joint Conference on Artificial Intelligence, pages 1027-1036, Seattle, WA, 2001.
18. J. M. Mooij. libdai 0.2.2: A free/open source c++ library for discrete approximate inference methods, 2008. URL http://www.libdai.org.
19. D. Mookherjee and B. Sopher. Learning behavior in an experimental matching pennies game. Games and Economic Behavior, 7(1):62-91, 1994.
20. Y. Shoham and M. Tennenholtz. On the emergence of social conventions: Modeling, analysis, and simulations. Artificial Intelligence, 94:139-166, 1997.
21. K. Tuyls and S. Parsons. What evolutionary game theory tells us about multiagent learning. Artificial Intelligence, 171:406-416, 2007.