Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Evolving teamwork and coordination with genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Evolving an expert checkers playing program without using humanexpertise
IEEE Transactions on Evolutionary Computation
Evolving neural networks to play checkers without relying on expert knowledge
IEEE Transactions on Neural Networks
A case-based approach for coordinated action selection in robot soccer
Artificial Intelligence
Reinforcement learning for robot soccer
Autonomous Robots
Analysis of strategy in robot soccer game
Neurocomputing
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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.