Learning in multiagent systems: An introduction from a game-theoretic perspective

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2636, Issue , 2003, Pages 202-215

  Vidal, José M ^a  

^a University of South Carolina   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; HAMILTONIANS; LEARNING SYSTEMS; PROBABILITY; ROBOTICS; SET THEORY;

ITERATED DOMINANCE APPROACH; LEARNING AGENTS; NASH EQUILIBRIUM; UTILITY THEORY;

MULTI AGENT SYSTEMS;

EID: 85085785819     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/3-540-44826-8_13     Document Type: Conference Paper

References (12)

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5. Gmytrasiewicz, P.J., Durfee., E.H.: A rigorous, operational formalization of recursive modeling. In: Proceedings of the First International Conference on Multi-Agent Systems. (1995) 125-132
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10. Vidal, J.M., Durfee, E.H.: The moving target function problem in multi-agent learning. In: Proceedings of the Third International Conference on Multi-Agent Systems. (1998)
11. Vidal, J.M., Durfee, E.H.: Learning nested models in an information economy. Journal of Experimental and Theoretical Artificial Intelligence 10 (1998) 291-308
12. Vidal, J.M., Durfee, E.H.: Predicting the expected behavior of agents that learn about agents: the CLRI framework. Autonomous Agents and Multi-Agent Systems (2002)