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Volumn , Issue , 2004, Pages 185-192

Communication complexity as a lower bound for learning in games

Author keywords

[No Author keywords available]

Indexed keywords

COMMUNICATION COMPLEXITY; COMMUNICATION PROTOCOL; LEARNING COMMUNITY; PAYOFFS;

EID: 14344252185     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (33)

References (20)
  • 2
    • 0036531878 scopus 로고    scopus 로고
    • Multiagent learning using a variable learning rate
    • Bowling, M., & Veloso, M. (2002). Multiagent learning using a variable learning rate. Artificial Intelligence, 136, 215-250.
    • (2002) Artificial Intelligence , vol.136 , pp. 215-250
    • Bowling, M.1    Veloso, M.2
  • 3
    • 1942421183 scopus 로고    scopus 로고
    • AWESOME: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents
    • Conitzer, V., & Sandholm, T. (2003). AWESOME: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents. International Conference on Machine Learning (pp. 83-90).
    • (2003) International Conference on Machine Learning , pp. 83-90
    • Conitzer, V.1    Sandholm, T.2
  • 4
    • 0002267135 scopus 로고    scopus 로고
    • Adaptive game playing using multiplicative weights
    • Freund, Y., & Schapire, R. E. (1999). Adaptive game playing using multiplicative weights. Games & Econ. Behavior, 29, 79-103.
    • (1999) Games & Econ. Behavior , vol.29 , pp. 79-103
    • Freund, Y.1    Schapire, R.E.2
  • 8
    • 0001976283 scopus 로고
    • Approximation to Bayes risk in repeated play
    • Hannan, J. (1957). Approximation to Bayes risk in repeated play, vol. III of Contributions to the Theory of Games, 97-139.
    • (1957) Contributions to the Theory of Games , vol.3 , pp. 97-139
    • Hannan, J.1
  • 9
    • 0000929496 scopus 로고    scopus 로고
    • Multiagent reinforcement learning: Theoretical framework and an algorithm
    • Hu, J., & Wellman, M. P. (1998). Multiagent reinforcement learning: Theoretical framework and an algorithm. International Conference on Machine Learning (pp. 242-250).
    • (1998) International Conference on Machine Learning , pp. 242-250
    • Hu, J.1    Wellman, M.P.2
  • 14
    • 85149834820 scopus 로고
    • Markov games as a framework for multi-agent reinforcement learning
    • Littman, M. L. (1994). Markov games as a framework for multi-agent reinforcement learning. International Conference on Machine Learning (pp. 157-163).
    • (1994) International Conference on Machine Learning , pp. 157-163
    • Littman, M.L.1
  • 15
    • 0034836562 scopus 로고    scopus 로고
    • Algorithms, games and the Internet
    • Papadimitriou, C. (2001). Algorithms, games and the Internet. STOC (pp. 749-753).
    • (2001) STOC , pp. 749-753
    • Papadimitriou, C.1
  • 17
    • 1942452233 scopus 로고    scopus 로고
    • Learning to cooperate in a social dilemma: A satisficing approach to bargaining
    • Stimpson, J., & Goodrich, M. (2003). Learning to cooperate in a social dilemma: A satisficing approach to bargaining. International Conference on Machine Learning (pp. 728-735).
    • (2003) International Conference on Machine Learning , pp. 728-735
    • Stimpson, J.1    Goodrich, M.2
  • 18
    • 85152198941 scopus 로고
    • Multi-agent reinforcement learning: Independent vs. cooperative agents
    • Tan, M. (1993). Multi-agent reinforcement learning: Independent vs. cooperative agents. International Conference on Machine Learning (pp. 330-337).
    • (1993) International Conference on Machine Learning , pp. 330-337
    • Tan, M.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.