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

Sam Devlin, Anastasija Anspoka, Nicholas John Sephton, Peter Ivan Cowling, Jeff Rollason

Research output: Contribution to conferencePaperpeer-review


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.
Original languageEnglish
Publication statusPublished - 19 Sept 2016
EventTwelfth Artificial Intelligence and Interactive Digital Entertainment Conference -
Duration: 8 Oct 201612 Oct 2016


ConferenceTwelfth Artificial Intelligence and Interactive Digital Entertainment Conference
Abbreviated titleAIIDE
Internet address

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