Evolution of cooperative behavior in a spatial iterated prisoner's dilemma game with different representation schemes of game strategies

Abstract

The iterated prisoner's dilemma (IPD) game has been frequently used to examine the evolution of cooperative behavior among agents in the field of evolutionary computation. A number of factors are known to be related to the evolution of cooperative behavior. One well-known factor is spatial relations among agents. The IPD game is often played in a grid-world. Such a spatial IPD game has a neighborhood structure which is used for local opponent selection in the IPD game and local parent selection in genetic operations. Another important factor is the choice of a representation scheme to encode each strategy. Different representation schemes often lead to totally different results. Whereas the choice of a representation scheme is known to be important, a mixture of different representation schemes has not been examined for the spatial IPD game in the literature. This means that a population of homogeneous agents with the same representation scheme has been assumed. In this paper, we introduce a different situation to the spatial IPD game in order to examine the evolution of cooperative behavior under more general assumptions. The main novelty of our spatial IPD game is the use of a mixture of different representation schemes. This means that we use a population of inhomogeneous agents with different representation schemes. Another novelty is the use of two neighborhood structures, each of which is used for local opponent selection and local parent selection. Under these specifications, we show a number of interesting observations on the evolution of cooperative behavior.

PSO Algorithm for IPD Game

“…The classical IPD model is extended to bridge the gap between the model and real-world dilemmas, especially about social and economic ones. One of them is the consideration of various situations such as a multiplayer version [6], a spatial version [14][15][16], stochastic strategies [17], random pairing [18], multiple objectives [19], noisy games [20], Q-Learning [21] and the trust based model [22,[28][29][30][31].…”

Design of prisoner's dilemma-based fuzzy C-means computed torque controller for PUMA-560 robot manipulator

“…C and D, cooperation and defection are two strategies that can be selected by each player in each round and T > R > P > S. For e.g., two (A1 and A2) players are chosen to play, the entries of the payoff matrix are interpreted as follows (Ishibuchi et al, 2009;Számadó et al, 2008):…”