Combining Gameplay Data with Monte Carlo Tree Search to Emulate Human Play


  • Sam Devlin University of York
  • Anastasija Anspoka University of York
  • Nick Sephton University of York
  • Peter Cowling University of York
  • Jeff Rollason AI Factory Ltd.



Monte Carlo Tree Search, Imitation Learning, Digital Games


Monte Carlo Tree Search (MCTS) has become a popular solution for controlling non-player characters. Its use has repeatedly been shown to be capable of creating strong game playing opponents. However, the emergent playstyle of agents using MCTS is not necessarily human-like, believable or enjoyable. AI Factory Spades, currently the top rated Spades game in the Google Play store, uses a variant of MCTS to control non-player characters. In collaboration with the developers, we collected gameplay data from 27,592 games and showed in a previous study that the playstyle of human players significantly differed from that of the non-player characters. This paper presents a method of biasing MCTS using human gameplay data to create Spades playing agents that emulate human play whilst maintaining a strong, competitive performance. The methods of player modelling and biasing MCTS presented in this study are generally applicable to digital games with discrete actions.




How to Cite

Devlin, S., Anspoka, A., Sephton, N., Cowling, P., & Rollason, J. (2021). Combining Gameplay Data with Monte Carlo Tree Search to Emulate Human Play. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 12(1), 16-22.