Predicting Game Difficulty and Engagement Using AI Players

Shaghayegh Roohi, Christian Guckelsberger, Asko Relas, Henri Heiskanen, Jari Takatalo, Perttu Hämäläinen

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

5 Sitaatiot (Scopus)
282 Lataukset (Pure)


This paper presents a novel approach to automated playtesting for the prediction of human player behavior and experience. We have previously demonstrated that Deep Reinforcement Learning (DRL) game-playing agents can predict both game difficulty and player engagement, operationalized as average pass and churn rates. We improve this approach by enhancing DRL with Monte Carlo Tree Search (MCTS). We also motivate an enhanced selection strategy for predictor features, based on the observation that an AI agent's best-case performance can yield stronger correlations with human data than the agent's average performance. Both additions consistently improve the prediction accuracy, and the DRL-enhanced MCTS outperforms both DRL and vanilla MCTS in the hardest levels. We conclude that player modelling via automated playtesting can benefit from combining DRL and MCTS. Moreover, it can be worthwhile to investigate a subset of repeated best AI agent runs, if AI gameplay does not yield good predictions on average.
JulkaisuProceedings of the ACM on Human-Computer Interaction
DOI - pysyväislinkit
TilaJulkaistu - syysk. 2021
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu
TapahtumaACM SIGCHI Annual Symposium on Computer-Human Interaction in Play - Vienna, Itävalta
Kesto: 18 lokak. 202121 lokak. 2021
Konferenssinumero: 8


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