AI Authoring for Virtual Characters in Conflict

Abstract

Game developers strive to have engaging believable characters in their work. One of the elements that has been pointed out as contributing to believability is social behavior. A category of social behavior is interpersonal conflict. In our current research we compare two AI approaches to model NPC conflict resolution strategies: one using the reactive planning language ABL and another using the AI framework FAtiMA. We identify the following metrics to evaluate social behavior modeling: mapping theory, emotion, model checking, variability, policy change. In our analysis we found it was easier to map conflict concepts in ABL and the model checking process was faster. FAtiMA had better support for emotion and other emergent attributes.