AAAI Publications, Seventh Artificial Intelligence and Interactive Digital Entertainment Conference

Font Size: 
Learning and Evaluating Human-Like NPC Behaviors in Dynamic Games
Yu-Han Chang, Rajiv Maheswaran, Tomer Levinboim, Vasudev Rajan

Last modified: 2011-10-09


We address the challenges of evaluating the fidelity of AI agents that are attempting to produce human-like behaviors in games. To create a believable and engaging game play experience, designers must ensure that their non-player characters (NPCs) behave in a human-like manner. Today, with the wide popularity of massively-multi-player online games, this goal may seem less important. However, if we can reliably produce human-like NPCs, this can open up an entirely new genre of game play. In this paper, we focus on emulating human behaviors in strategic game settings, and focus on a Social Ultimatum Game as the testbed for developing and evaluating a set of metrics for comparing various autonomous agents to human behavior collected from live experiments.


adaptive agents, game theory, Ultimatum game, behavior modeling

Full Text: PDF