research-article

Predicting Game Difficulty and Engagement Using AI Players

Authors:

Shaghayegh Roohi,

Christian Guckelsberger,

Henri Heiskanen,

Perttu HämäläinenAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 5, Issue CHI PLAY

Article No.: 231, Pages 1 - 17

https://doi.org/10.1145/3474658

Published: 06 October 2021 Publication History

Abstract

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.

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Kristensen JBurelli P(2024)Difficulty Modelling in Mobile Puzzle GamesInternational Journal of Computer Games Technology10.1155/2024/55923732024Online publication date: 1-Jan-2024
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Index Terms

Predicting Game Difficulty and Engagement Using AI Players
1. Computing methodologies
  1. Modeling and simulation
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User models

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 5, Issue CHI PLAY

CHI PLAY

September 2021

1535 pages

EISSN:2573-0142

DOI:10.1145/3490463

Editors:
Kathrin Gerling
KU Leuven
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Elisa Mekler
Aalto University
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Regan Mandryk
University of Saskatchewan

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Published: 06 October 2021

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Sha RShi W(2025)The enduring appeal of Mahjong: Navigating the challenges of AI integrationEntertainment Computing10.1016/j.entcom.2024.10091552(100915)Online publication date: Jan-2025
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https://dl.acm.org/doi/10.1155/2024/5592373
Bilius LSchipor OVatavu R(2024)The Age-Reward Perspective: A Systematic Review of Reward Mechanisms in Serious Games for Older PeopleProceedings of the 2024 ACM International Conference on Interactive Media Experiences10.1145/3639701.3656327(168-181)Online publication date: 7-Jun-2024
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Fryer LShum A(2024)Math task experiences and motivation to learn more: How prior knowledge and interest interact with Task‐Interest & Task‐Difficulty perceptions and feed a desire to reengageBritish Educational Research Journal10.1002/berj.405751:1(49-72)Online publication date: Sep-2024
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