AAAI Publications, The Twenty-Sixth International FLAIRS Conference

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Learning from Demonstration to Be a Good Team Member in a Role Playing Game
Michael Silva, Silas McCroskey, Jonathan Rubin, Michael Youngblood, Ashwin Ram

Last modified: 2013-05-19

Abstract


We present an approach that uses learning from demonstration in a computer role playing game to create a controller for a companion team member. We describe a behavior engine that uses case-based reasoning. The behavior engine accepts observation traces of human playing decisions and produces a sequence of actions which can then be carried out by an artificial agent within the gaming environment. Our work focuses on team-based role playing games, where the agents produced by the behavior engine act as team members within a mixed human-agent team. We present the results of a study we conducted, where we assess both the quantitative and qualitative performance difference between human-only teams compared with hybrid human-agent teams. The results of our study show that human-agent teams were more successful at task completion and, for some qualitative dimensions, hybrid teams were perceived more favorably than human-only teams.

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