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Volumn 4212 LNAI, Issue , 2006, Pages 6-17

Learning in one-shot strategic form games

Author keywords

[No Author keywords available]

Indexed keywords

GAME THEORY; INTELLIGENT AGENTS; MATHEMATICAL MODELS; MULTI AGENT SYSTEMS;

EID: 33750361495     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11871842_6     Document Type: Conference Paper
Times cited : (15)

References (19)
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    • Erev, I., Roth, A.: Predicting how people play games: Reinforcement learning in games with unique strategy equilibrium. American Economic Review 88 (1998) 848-881
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    • Erev, I.1    Roth, A.2
  • 3
    • 0008614785 scopus 로고    scopus 로고
    • The dynamics of reinforcement learning in cooperative multi-agent systems
    • Claus, C., Boutilier, C.: The dynamics of reinforcement learning in cooperative multi-agent systems. In: Proc. Workshop on Multi-Agent Learning. (1997) 602-608
    • (1997) Proc. Workshop on Multi-agent Learning , pp. 602-608
    • Claus, C.1    Boutilier, C.2
  • 5
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    • Markov games as a framework for multi-agent reinforcement learning
    • Littman, M.L.: Markov games as a framework for multi-agent reinforcement learning. In: Proc. 11th ICML. (1994) 157-163
    • (1994) Proc. 11th ICML , pp. 157-163
    • Littman, M.L.1
  • 6
    • 0000929496 scopus 로고    scopus 로고
    • Multi-agent reinforcement learning: Theoretical framework and an algorithms
    • Hu, J., Wellman, M.: Multi-agent reinforcement learning: Theoretical framework and an algorithms. In: Proc. 15th ICML. (1998)
    • (1998) Proc. 15th ICML
    • Hu, J.1    Wellman, M.2
  • 7
    • 84880854156 scopus 로고    scopus 로고
    • R-max - A general polynomial time algorithm for near-optimal reinforcement learning
    • Brafman, R.I., Tennenholtz, M.: R-max - a general polynomial time algorithm for near-optimal reinforcement learning. In: IJCAI'01. (2001)
    • (2001) IJCAI'01
    • Brafman, R.I.1    Tennenholtz, M.2
  • 8
    • 0033423368 scopus 로고    scopus 로고
    • Exploration strategies for model-based learning in multiagent systems
    • Carmel, D., Markovitch, S.: Exploration strategies for model-based learning in multiagent systems. Autonomous Agents and Multi-agent Systems 2(2) (1999) 141-172
    • (1999) Autonomous Agents and Multi-agent Systems , vol.2 , Issue.2 , pp. 141-172
    • Carmel, D.1    Markovitch, S.2
  • 10
    • 58149324992 scopus 로고
    • Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term
    • Roth, A., Erev, I.: Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term. Games and Economic Behavior 8 (1995) 164-212
    • (1995) Games and Economic Behavior , vol.8 , pp. 164-212
    • Roth, A.1    Erev, I.2
  • 12
    • 0001635606 scopus 로고    scopus 로고
    • Cognition and behavior in normal-form games: An experimental study
    • Costa-Gomes, M., Crawford, V.P., Broseta, B.: Cognition and behavior in normal-form games: An experimental study. Econometrica 69(5) (2001) 1193-1235
    • (2001) Econometrica , vol.69 , Issue.5 , pp. 1193-1235
    • Costa-Gomes, M.1    Crawford, V.P.2    Broseta, B.3
  • 14
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    • Population rule learning in symmetric normal-form games: Theory and evidence
    • Stahl, D.O.: Population rule learning in symmetric normal-form games: theory and evidence. Journal of Economic Behavior and Organization 1304 (2001) 1-14
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    • Stahl, D.O.1
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    • Run the gamut: A comprehensive approach to evaluating game-theoretic algorithms
    • Nudelman, E., Wortman, J., Shoham, Y., Leyton-Brown, K.: Run the gamut: A comprehensive approach to evaluating game-theoretic algorithms. In: AAMAS '04. (2004) 880-887
    • (2004) AAMAS '04 , pp. 880-887
    • Nudelman, E.1    Wortman, J.2    Shoham, Y.3    Leyton-Brown, K.4
  • 18


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.