Automated playtesting with procedural personas through MCTs with evolved heuristics

Christoffer Holmgård, Michael Cerny Green, Antonios Liapis, Julian Togelius

    Research output: Contribution to journalArticlepeer-review


    This paper describes a method for generative player modeling and its application to the automatic testing of game content using archetypal player models called procedural personas. Theoretically grounded in psychological decision theory, procedural personas are implemented using a variation of Monte Carlo tree search (MCTS) where the node selection criteria are developed using evolutionary computation, replacing the standard UCB1 criterion of MCTS. Using these personas, we demonstrate how generative player models can be applied to a varied corpus of game levels and demonstrate how different playstyles can be enacted in each level. In short, we use artificially intelligent personas to construct synthetic playtesters. The proposed approach could be used as a tool for automatic play testing when human feedback is not readily available or when quick visualization of potential interactions is necessary. Possible applications include interactive tools during game development or procedural content generation systems where many evaluations must be conducted within a short time span.

    Original languageEnglish (US)
    Article number2808198
    Pages (from-to)352-362
    Number of pages11
    JournalIEEE Transactions on Games
    Issue number4
    StatePublished - Dec 2019


    • Agent controllers
    • Automated playtesting
    • Play persona
    • Player modeling

    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence
    • Electrical and Electronic Engineering
    • Control and Systems Engineering


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