Autonomously acquiring a video game agent's behavior: letting players feel like playing with a human player

Authors:
Nobuto Fujii;Yuichi Sato;Hironori Wakama;Haruhiro Katayose
Affiliations:
Graduate School of Science and Technology, Kwansei Gakuin University, Sanda, Hyogo, Japan;Graduate School of Science and Technology, Kwansei Gakuin University, Sanda, Hyogo, Japan;Graduate School of Science and Technology, Kwansei Gakuin University, Sanda, Hyogo, Japan;Graduate School of Science and Technology, Kwansei Gakuin University, Sanda, Hyogo, Japan
Venue:
ACE'12 Proceedings of the 9th international conference on Advances in Computer Entertainment
Year:
2012

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Abstract

Designing behavior patterns of video game agents (COM players) is a crucial aspect of video game development. While various systems aiming to automatically acquire behavior patterns has been proposed and some have successfully obtained stronger patterns than human players, the obtained behavior patterns looks mechanical. We present herein an autonomous acquisition of video game agent behavior, which emulates the behavior of a human player. Instead of implementing straightforward heuristics, the behavior is acquired using Q-learning, a reinforcement learning, where, biological constraints are imposed. In the experiments using Infinite Mario Bros., we observe that behaviors that imply human behaviors are obtained by imposing sensory error, perceptual and motion delay, and fatigue as biological constraints.