PSO approaches to coevolve IPD strategies

Abstract

This paper investigates two different approaches using particle swarm optimisation (PSO) to evolve strategies for iterated prisoner's dilemma (IPD). Strategies evolved by the lesser known binary PSO algorithm are compared to strategies evolved by neural networks that were trained using PSO. Evolved strategies are compared against well-known game theory strategies, with positive results. The presence of noise during IPD interactions are also investigated, and evolved strategies are compared against the same well-known game theory strategies in a noisy environment.