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Evolutionary Markov Games Based on Neural Network

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Advances in Neural Networks – ISNN 2009 (ISNN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5553))

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Abstract

Based on the dynamic characteristics of evolutionary game and Markov process, this paper presents a dynamic decision model for evolutionary Markov games. In this model, players’ strategy-choosing is mapped to a Markov decision process with payoffs, and transition probability is made by Boltzmann distribution. This paper uses neural network to simulate strategy-choosing in evolutionary Markov games, Experimental results show that the neural network can successfully simulate players’ dynamic learning and actions in evolutionary Markov games.

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References

  1. Smith, J.M.: Evolution and the Theory of Games. Cambridge University Press, Cambridge (1982)

    Book  MATH  Google Scholar 

  2. Yao, X., Darwen, P.: Genetic Algorithms and Evolutionary Games. In: Barnett, W., Chiarella, C., Keen, S., Marks, R., Schnabl, H. (eds.) Commerce, Complexity and Evolution, pp. 313–333. Cambridge University Press, Cambridge (2000)

    Chapter  Google Scholar 

  3. Thlol, Y., Acan, A.: Ants Can Play Prisoner’s Dilemma. In: IEEE International Conference on Systems. Man and Cybernetics, pp. 1348–1354 (2003)

    Google Scholar 

  4. Amir, M., Berninghaus, S.K.: Another Approach to Mutation and Learning. Games and Economic Behavior 14, 19–43 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  5. Tadj, L., Touzene, A.: A QBD Approach to Evolutionary Game Theory. Applied Mathematical Modelling 27, 913–927 (2003)

    Article  MATH  Google Scholar 

  6. Arditi, D., Oksay, F.E., Tohdemir, O.B.: Predicting the Outcome of Construction Litigation Using Neural Network. Computer Aided Civil and Infrastructure Engineering 13(4), 75–81 (1998)

    Article  Google Scholar 

  7. Ghezzi, D., Pedrocchi, A., Menegon, A.: PhotoMEA: An Opto-Electronic Biosensor for Monitoring in Vitro Neuronal Network Activity. Biosystems 78(7), 150–155 (2007)

    Article  Google Scholar 

  8. Kim, Y.H., Lewis, F.L.: Neural Network Output Feedback Control of Robot Manipulators. IEEE Transactions on Robotics and Automation 15(2), 301–309 (1999)

    Article  Google Scholar 

  9. Gu, D., Hu, H.: Neural predictive control for a car-likemobile robot. Robotics and Autonomous Systems 39(5), 73–86 (2002)

    Article  Google Scholar 

  10. Huang, H., Liang, C.: Strategy-Based Decision Making of a Soccer Robot System Using a Real-time Self-organizing Fuzzy Decision Tree. Fuzzy Sets and Systems 127(3), 49–64 (2002)

    Article  MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Political Science and Public Management, Wuhan University, Wuhan, 430072, China

    Liu Weibing

  2. Economics and Management School of Wuhan University, Wuhan, 430072, China

    Wang Xianjia

  3. Institute of system engineering, Wuhan University, Wuhan, 430072, China

    Huang Binbin

Authors
  1. Liu Weibing
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  2. Wang Xianjia
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  3. Huang Binbin
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Editor information

Editors and Affiliations

  1. Departamento de Control Automático, CINVESTAV-IPN, A.P. 14-740, Av.IPN 2508, 07360, México D.F., México

    Wen Yu

  2. Deptartment of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, USA

    Haibo He

  3. Dept. of Electrical and Computer Engineering, South Dakota School of Mines & Technology, 501 E. St. Joseph Street, SD 57701, Rapid City, USA

    Nian Zhang

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© 2009 Springer-Verlag Berlin Heidelberg

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Weibing, L., Xianjia, W., Binbin, H. (2009). Evolutionary Markov Games Based on Neural Network. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_12

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  • DOI: https://doi.org/10.1007/978-3-642-01513-7_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01512-0

  • Online ISBN: 978-3-642-01513-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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