Performance evaluation of an evolutionary method for RoboCup soccer strategies

This paper proposes an evolutionary method for acquiring team strategies of RoboCup soccer agents. The action of an agent in a subspace is specified by a set of action rules. The antecedent part of action rules includes the position of the agent and the distance to the nearest opponent. The consequent part indicates the action that the agent takes when the antecedent part of the action rule is satisfied. The action of each agent is encoded into an integer string that represents the action rules. A chromosome is the concatenated string of integer strings for all agents. We employ an ES-type generation update scheme after producing new integer strings by using crossover and mutation. Through computer simulations, we show the effectiveness of the proposed method.