Player-Centered AI for Automatic Game Personalization: Open Problems
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Abstract (arXiv)
Computer games represent an ideal research domain for the next generation of personalized digital applications. This paper presents a player-centered framework of AI for game personalization, complementary to the commonly used system-centered approaches. Built on the Structure of Actions theory, the paper maps out the current landscape of game personalization research and identifies eight open problems that need further investigation. These problems require deep collaboration between technological advancement and player experience design.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 4 of its 78 references that are in the map. First posted on arXiv 15 Feb 2021.
Builds on
4 of 78 references.
References · 4
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
- 2016European Union regulations on algorithmic decision-making and a "right to explanation"cited by 343
- 2019Tracing Player Knowledge in a Parallel Programming Educational Gamecited by 5
- 2019Experience Management in Multi-player Gamescited by 3
Built on by
Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 0; 2025: 0; 2026: 1.
3 papers.
- 2026AI for Games in the Foundation Model Era15 Sep 2026
- 2023Improving Fairness in Adaptive Social Exergames via Shapley Bandits18 Feb 2023
- 2021Open Player Modeling: Empowering Players through Data Transparency12 Oct 2021