Autonomous Virtual Player in a Video Game Imitating Human Players: The ORION Framework

Abstract

This paper introduces the design of autonomous virtual player based on imitation learning using human behavior observations. The ORION model provides both data mining techniques allowing the extraction of knowledge and behavior models allowing the control of the autonomous behaviors. ORION is also an operational tool allowing the representation, transformation, visualization and prediction of data. We illustrate the use of our model by detailing the implementation of a virtual player for the video game Unreal Tournament 3. Thanks to ORION, data from low level behaviors were collected through three scenarios performed by human players: movement, long range aiming and close combat. Behaviors can then be learned from the obtained data-sets after transformations and application of data mining techniques. ORION allows us to build a complete behavior using an extension of a Behavior Tree integrating ad hoc features in order to manage aspects of behavior that we have not been able to learn automatically.