Integrating Monte Carlo Tree Search with Knowledge-Based Methods to Create Engaging Play in a Commercial Mobile Game

Authors

  • Daniel Whitehouse University of York
  • Peter Cowling University of York
  • Edward Powley University of York
  • Jeff Rollason AI Factory Ltd.

Keywords:

Game tree search, hidden information, Monte Carlo methods, Monte Carlo Tree Search

Abstract

Monte Carlo Tree Search (MCTS) has produced many recent breakthroughs in game AI research, particularly in computer Go. In this paper we consider how MCTS can be applied to create engaging AI for a popular commercial mobile phone game: Spades by AI Factory, which has been downloaded more than 2.5 million times. In particular, we show how MCTS can be integrated with knowledge-based methods to create an interesting, fun and strong player which makes far fewer plays that could be perceived by human observers as blunders than MCTS without the injection of knowledge. These blunders are particularly noticeable for Spades, where a human player must co-operate with an AI partner. MCTS gives objectively stronger play than the knowledge-based approach used in previous versions of the game and offers the flexibility to customise behaviour whilst maintaining a reusable core, with a reduced development cycle compared to purely knowledge-based techniques.

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Published

2021-06-30

How to Cite

Whitehouse, D., Cowling, P., Powley, E., & Rollason, J. (2021). Integrating Monte Carlo Tree Search with Knowledge-Based Methods to Create Engaging Play in a Commercial Mobile Game. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 9(1), 100-105. Retrieved from https://ojs.aaai.org/index.php/AIIDE/article/view/12679