CAPIR: Collaborative Action Planning with Intention Recognition


  • Truong-Huy Nguyen National University of Singapore
  • David Hsu National University of Singapore
  • Wee-Sun Lee National University of Singapore
  • Tze-Yun Leong National University of Singapore
  • Leslie Kaelbling Massachusetts Institute of Technology
  • Tomas Lozano-Perez Massachusetts Institute of Technology
  • Andrew Grant Singapore-MIT GAMBIT Game Lab



action planning, MDP, POMDP, plan recognition


We apply decision theoretic techniques to construct non-player characters that are able to assist a human player in collaborative games. The method is based on solving Markov decision processes, which can be difficult when the game state is described by many variables. To scale to more complex games, the method allows decomposition of a game task into subtasks, each of which can be modelled by a Markov decision process. Intention recognition is used to infer the subtask that the human is currently performing, allowing the helper to assist the human in performing the correct task. Experiments show that the method can be effective, giving near-human level performance in helping a human in a collaborative game.




How to Cite

Nguyen, T.-H., Hsu, D., Lee, W.-S., Leong, T.-Y., Kaelbling, L., Lozano-Perez, T., & Grant, A. (2011). CAPIR: Collaborative Action Planning with Intention Recognition. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 7(1), 61-66.