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

Authors

  • 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.

DOI:

https://doi.org/10.1609/aiide.v12i1.12858

Keywords:

Monte Carlo Tree Search, Imitation Learning, Digital Games

Abstract

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.

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Published

2021-06-25

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. https://doi.org/10.1609/aiide.v12i1.12858