TAP: An effective personality representation for inter-Agent adaptation in games

Abstract

Tactical Agent Personality (TAP) is a modeling concept to capture tactical patterns in game agents, based on a personality concept introduced by Tan and Cheng (2007), to allow behavior adaptation to different play styles. We introduced a weighted sequential topology to the actions set to capture tactical preferences rather than individual action preferences. This produces a personality representation of much higher expressive power that improves the adaptation performance and subsequently enables a larger variety of action genres to be adaptable. A TAP-based learning framework is then constructed and it is shown to perform better than the one based on the previous agent personality. Consequently, we also implement an RPG scenario that demonstrates its ability to generate adaptive plausible behavior in a much larger variety of action genres. Copyright © 2008, Association for the Advancement of Artificial Intelligence.