An Automated Model-Based Adaptive Architecture in Modern Games


  • Chek Tien Tan DigiPen Institute of Technology, Singapore
  • Ho-lun Cheng National University of Singapore



Game AI, POMDP, MDP, Knowledge Representation


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 which has shown to provide plausible adaptive decision making in modern games. However, it suffers from highly time-consuming manual model specification problems. By incorporating an automated priority sweeping based model builder for the MDP, as well as using the Tactical Agent Personality 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.




How to Cite

Tan, C. T., & Cheng, H.- lun. (2010). An Automated Model-Based Adaptive Architecture in Modern Games. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 6(1), 186-191.