Learning Human Behavior from Observation for Gaming Applications

The gaming industry has reached a point where improving graphics has only a small effect on how much a player will enjoy a game. The focus has turned to adding more humanlike characteristics into computer game agents. Machine learning techniques are scarcely being used in games, although they do offer powerful means for creating humanlike behaviors in agents. The first person shooter (FPS), Quake 2, is an open source game that offers a multi-agent environment in which to create game agents (bots). The work described in this paper seeks to combine neural networks with a modeling paradigm known as context based reasoning (CxBR) to create a contextual game observation (CONGO) system that produces humanlike Quake 2 bots. A default level of intelligence is instilled into the bots through contextual scripts to prevent the bot from being trained to be completely useless. The results show that the humanness and entertainment value as compared to a traditional scripted bot have improved, although, CONGO bots usually ranked only slightly above a novice skill level. Overall, CONGO offers the gaming community a mode of game play that has promising entertainment value.