Combining cooperative and adversarial coevolution in the context of pac-man

In this paper we discuss our recent approach for evolving a diverse set of agents for both the Pac-Man and the Ghost Team track of the current Ms. Pac-Man vs. Ghost Team competition. We used genetic programming for generating various agents, which were distributed in multiple populations. The optimization includes cooperative and adversarial subtasks, such that Pac-Man is constantly competing against the Ghost Team, whereas the Ghost Team is formed of four cooperatively evolving populations. For the generation of a Ghost Team and calculation of the associated fitness we took one individual from each population. This strict separation preserves the evolution pressure for each population such that respective Ghost Teams compete against each other in developing an efficient cooperation in catching Pac-Man. This approach not only is useful for developing a versatile set of playing agents, but also for adapting the team to the current behavior of the competing populations. Ultimately, we aim for optimizing both tasks in parallel.