Hierarchical Plan Representations for Encoding Strategic Game AI


  • Hai Hoang Lehigh University
  • Stephen Lee-Urban Lehigh University
  • Héctor Muñoz-Avila Lehigh University




In this paper we explore the use of Hierarchical-Task-Network (HTN) representations to model strategic game AI. We will present two case studies. The first one reports on an experiment using HTNs to model strategies for Unreal Tournament® (UT) bots. We will argue that it is possible to encode strategies that coordinate teams of bots in first-person shooter games using HTNs. The second one compares an alternative to HTNs called Task-Method-Knowledge (TMK) process models. TMK models are of interest to game AI because, as we will show, they are as expressive as HTNs but have more convenient syntax. Therefore, HTN planners can be used to generate correct plans for coordinated team AI behavior modeled with TMK representations.




How to Cite

Hoang, H., Lee-Urban, S., & Muñoz-Avila, H. (2021). Hierarchical Plan Representations for Encoding Strategic Game AI. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 1(1), 63-68. https://doi.org/10.1609/aiide.v1i1.18717