Co-Evolutionary Optimization of Autonomous Agents in a Real-Time Strategy Game

This paper presents an approach based in an evolutionary algorithm, aimed to improve the behavioral parameters which guide the actions of an autonomous agent (bot) inside the real-time strategy game, Planet Wars. The work describes a co-evolutionary implementation of a previously presented method GeneBot, which yielded successful results, but focused in 4vs matches this time. Thus, there have been analyzed the effects of considering several individuals to be evolved (improved) at the same time in the algorithm, along with the use of three different fitness functions measuring the goodness of each bot in the evaluation. They are based in turns and position, and also in mathematical computations of linear regression and area regarding the number of ships belonging to the bot/individual to be evaluated. In addition, the variance of using an evolutionary algorithm with and without previous knowledge in the co-evolution phase is also studied, i.e., respectively using specific rivals to perform the evaluation, or just considering to this end individuals in the population being evolved. The aim of these co-evolutionary approaches are mainly two: first, reduce the computational time; and second find a robust fitness function to be used in the generation of evolutionary bots optimized for 4vs battles.