An evolutionary approach for interactive computer games

In this paper, we introduce the first stage of experiments on neuro-evolution mechanisms applied to predator/prey multicharacter computer games. Our test-bed is a computer game where the prey (i.e. player) has to avoid its predators by escaping through an exit without getting killed. By viewing the game from the predators' (i.e. opponents') perspective, we attempt offline to evolve neural-controlled opponents capable of playing effectively against computer-guided fixed strategy players. Their efficiency is based on cooperation which emerges from an abstract type of partial interaction with their environment. In addition, investigation of behavior generalization demonstrated the crucial contribution of playing strategies in the development of successful predator behaviors. However, emergent well-behaved opponents trained offline with fixed strategies do not make the game interesting to play. We therefore present an evolutionary mechanism for opponents that keep learning from a player while playing against it (i.e. online) and we demonstrate its efficiency and robustness in increasing the predators' performance while altering their behavior as long as the game is played. Computer game opponents following this online learning approach show high adaptability to changing player strategies, which provides evidence for the approach's effectiveness and interest against human players.