EvoCommander: A Novel Game Based on Evolving and Switching Between Artificial Brains

Abstract

Neuroevolution [i.e., evolving artificial neural networks (ANNs) through evolutionary algorithms] has shown promise in evolving agents and robot controllers, which display complex behaviors and can adapt to their environments. These properties are also relevant to video games, since they can increase their longevity and replayability. However, the design of most current games precludes the use of any techniques which might yield unpredictable or even open-ended results. This paper describes the game EvoCommander, with the goal to further demonstrate the potential of neuroevolution in games. In EvoCommander the player incrementally evolves an arsenal of ANN-controlled behaviors (e.g., ranged attack, flee, etc.) for a simple robot that has to battle other player and computer controlled robots. The game introduces the novel game mechanic of “brain switching,” selecting which evolved neural network is active at any point during battle. Results from playtests indicate that brain switching is a promising new game mechanic, leading to players employing interesting different strategies when training their robots and when controlling them in battle.