Neighborhood Specification for Game Strategy Evolution in a Spatial Iterated Prisoner's Dilemma Game

The prisoner's dilemma is a two-player non-zero-sum game. Its iterated version has been frequently used to examine game strategy evolution in the literature. In this paper, we discuss the setting of neighborhood structures in its spatial iterated version. The main characteristic feature of our spatial iterated prisoner's dilemma game model is that each cell has a different scheme to represent game strategies. In our computational experiments, one of four representation schemes is randomly assigned to each cell in a two-dimensional grid-world. An agent at each cell has a game strategy encoded by the assigned representation scheme. In this situation, an agent may have no neighbors with the same representation scheme as the agent's scheme. The existence of such an agent has a negative effect on the evolution of cooperative behavior. This is because strategies with different representation schemes cannot be recombined. When no neighbors have the same representation scheme as the agent's scheme, no recombination can be used for generating a new strategy for the agent. In our former study, we used a larger neighborhood structure for such an agent. As a result, each agent has a different neighborhood structure and a different number of neighbors. This makes it difficult to discuss the effect of the neighborhood size on the evolution of cooperative behavior. In this paper, we propose the use of the following setting: Each agent has the same number of neighbors with the same representation scheme as the agent's scheme. This means that each agent has the same number of qualified neighbors as its mates. We also examine a different spatial model where the location of each agent is randomly specified as a point in a two-dimensional continuous space instead of a grid-world.

[1]  Eun-Youn Kim,et al.  Understanding representational sensitivity in the iterated prisoner's dilemma with fingerprints , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[2]  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.

[3]  Hisao Ishibuchi,et al.  Strategy evolution in a spatial IPD game where each agent is not allowed to play against itself , 2012, 2012 IEEE Congress on Evolutionary Computation.

[4]  P. Grim Spatialization and greater generosity in the stochastic Prisoner's Dilemma. , 1996, Bio Systems.

[5]  Daniel A. Ashlock,et al.  Fingerprinting: Visualization and Automatic Analysis of Prisoner's Dilemma Strategies , 2008, IEEE Transactions on Evolutionary Computation.

[6]  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.

[7]  Zbigniew Skolicki,et al.  Improving Evolutionary Algorithms with Multi-representation Island Models , 2004, PPSN.

[8]  Sung-Bae Cho,et al.  The Impact of Payoff Function and Local Interaction on the N-Player Iterated Prisoner's Dilemma , 2000, Knowledge and Information Systems.

[9]  M. Oliphant Evolving cooperation in the non-iterated prisoner''s dilemma , 1994 .

[10]  Hisao Ishibuchi,et al.  Evolution of strategies in a spatial IPD game with a number of different representation schemes , 2012, 2012 IEEE Congress on Evolutionary Computation.

[11]  Brauchli,et al.  Evolution of cooperation in spatially structured populations , 1999, Journal of theoretical biology.

[12]  Daniel A. Ashlock,et al.  Fingerprint Analysis of the Noisy Prisoner's Dilemma Using a Finite-State Representation , 2007, IEEE Transactions on Computational Intelligence and AI in Games.