Evolving the cooperative behaviour in Unreal™ bots
Antonio Miguel Mora, Matthew Andres Moreno, Juan Julián Merelo, Pedro Ángel Castillo, M. G. Arenas, Juan Luís Jiménez Laredo · 2010
This paper presents an approach to the evolution of the cooperative behaviour of some bots inside the PC game Unreal™. We intend to create bots that cooperate as a team trying to beat other teams (composed of human players or bots). So, in addition to the improvement of the default artificial intelligence (AI) of bots, we have performed an improvement of the `team AI'. We have applied an evolutionary algorithm which optimizes the parameters considered in the hard-coded states inside the bot AI code, mainly those related to the cooperation. Two different approaches have been tested inside some different battle arenas: one considering a different set of parameters for every bot in the team, and the other one considering the same set of parameters for all the teammates. The results show that both methods yield better teams than the standard ones. The teams which share the same behaviour parameters, get a higher score than those with bots playing with different parameters.