Freedman

Planning is one of the oldest areas of research within artificial intelligence, studying the selection of actions for accomplishing goals. The more recently established areas of plan, activity, and intent recognition instead study an agent's behavior and task(s) given observations of its chosen actions. While these areas have been independently studied and applied to games in the past for both understanding player behavior and developing game characters, the potential for their integration presents even more opportunities via adaptive interaction with the player. In this manuscript, we discuss recent research on the integration of these areas and investigate potential uses for such integrated systems in games.