An automated model-based adaptive architecture in modern games

Abstract

This paper proposes an automatic model-based approach that enables adaptive decision making in modern virtual games. It builds upon the Integrated MDP and POMDP Learning AgeNT (IMPLANT) architecture (Tan and Cheng 2009) which has shown to provide plausible adaptive decision making in modern games. However, it suffers from highly timeconsuming manual model specification problems. By incorporating an automated priority sweeping based model builder for the MDP, as well as using the Tactical Agent Personality (Tan and Cheng 2007) for the POMDP, the work in this paper aims to resolve these problems. Empirical proof of concept is shown based on an implementation in a modern game scenario, whereby the enhanced IMPLANT agent is shown to exhibit superior adaptation performance over the old IMPLANT agent whilst eliminating manual model specifications and at the same time still maintaining plausible speeds. Copyright © 2010, Association for the Advancement of Artificial.