Co-evolutionary learning in the N-choice IPD game with PSO algorithm

A particle swarm optimization (PSO) approach towards the development of strategy co-evolution for multiple choices IPD game is presented. It is demonstrated that, birds can play IPD with multiple choices, and the co-evolutionary behaviors are influenced by social environment.

[1]  B. Majolo,et al.  Human friendship favours cooperation in the Iterated Prisoner's Dilemma , 2006 .

[2]  Weidong Zhu,et al.  Combined trust model based on evidence theory in iterated prisoner's dilemma game , 2011, Int. J. Syst. Sci..

[3]  Siang Yew Chong,et al.  Improving Generalization Performance in Co-Evolutionary Learning , 2012, IEEE Transactions on Evolutionary Computation.

[4]  Xin Yao,et al.  Why more choices cause less cooperation in iterated prisoner's dilemma , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[5]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[6]  Xin Yao,et al.  Multiple Choices and Reputation in Multiagent Interactions , 2007, IEEE Transactions on Evolutionary Computation.

[7]  Andries Petrus Engelbrecht,et al.  Particle swarm optimization approaches to coevolve strategies for the iterated prisoner's dilemma , 2005, IEEE Transactions on Evolutionary Computation.

[8]  Mario Giacobini,et al.  An Evolutionary Game-Theoretical Approach to Particle Swarm Optimisation , 2008, EvoWorkshops.

[9]  Yongling Zheng,et al.  On the convergence analysis and parameter selection in particle swarm optimization , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[10]  Peter Tiño,et al.  Relationship Between Generalization and Diversity in Coevolutionary Learning , 2009, IEEE Transactions on Computational Intelligence and AI in Games.

[11]  David B. Fogel,et al.  On the Relationship between the Duration of an Encounter and the Evolution of Cooperation in the Iterated Prisoner's Dilemma , 1995, Evolutionary Computation.

[12]  Peter Tiño,et al.  Measuring Generalization Performance in Coevolutionary Learning , 2008, IEEE Transactions on Evolutionary Computation.

[13]  Hisao Ishibuchi,et al.  Evolution of iterated prisoner's dilemma game strategies in structured demes under random pairing in game playing , 2005, IEEE Transactions on Evolutionary Computation.

[14]  Hisao Ishibuchi,et al.  Evolution of Strategies With Different Representation Schemes in a Spatial Iterated Prisoner's Dilemma Game , 2011, IEEE Transactions on Computational Intelligence and AI in Games.

[15]  Koichi Moriyama,et al.  Utility based Q-learning to facilitate cooperation in Prisoner's Dilemma games , 2009, Web Intell. Agent Syst..

[16]  Xin Yao,et al.  Behavioral diversity, choices and noise in the iterated prisoner's dilemma , 2005, IEEE Transactions on Evolutionary Computation.

[17]  Hisao Ishibuchi,et al.  Effects of configuration of agents with different strategy representations on the evolution of cooperative behavior in a spatial IPD game , 2011, 2011 IEEE Conference on Computational Intelligence and Games (CIG'11).

[18]  W. Hamilton,et al.  The evolution of cooperation. , 1984, Science.

[19]  Andries Petrus Engelbrecht,et al.  Comparing PSO structures to learn the game of checkers from zero knowledge , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[20]  Robert Axelrod,et al.  The Evolution of Strategies in the Iterated Prisoner's Dilemma , 2001 .

[21]  Xin Yao,et al.  Co-Evolution in Iterated Prisoner's Dilemma with Intermediate Levels of Cooperation: Application to Missile Defense , 2002, Int. J. Comput. Intell. Appl..