TAP: An Effective Personality Representation for Inter-Agent Adaptation in

Tactical Agent Personality (TAP) is a modeling con-cept 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 personal-ity 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 abil-ity to generate adaptive plausible behavior in a much larger variety of action genres