Towards Diverse Non-Player Character behaviour discovery in multi-agent environments

This paper introduces a method for developing diverse Non-Player Character (NPC) behaviour through a multiagent genetic algorithm based on Map-Elites. We examine the outcomes of implementing our system in a test environment, with a particular emphasis on the diversity of the evolved agents in the feature space. This research is motivated by how diverse NPCs are an important factor for improving player experience. We show how our multi agent map-elite algorithm is capable of isolating the evolved NPCs in the chosen feature space. Results showed that variation in agent fitness could be predicted with $40 \%$ from agent genomes, when agents played 100 games each.