# Player-Centered AI for Automatic Game Personalization: Open Problems

arXiv: https://arxiv.org/abs/2102.07548 · HTML: https://mlsanity.com/paper/2102.07548/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 78 references that are in the map.
- First posted on arXiv 15 Feb 2021.

## Builds on (most cited first, 4 of 4)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [Tracing Player Knowledge in a Parallel Programming Educational Game](https://mlsanity.com/paper/1908.05632/) (2019)
- [Experience Management in Multi-player Games](https://mlsanity.com/paper/1907.02349/) (2019)

## Built on by (most cited first, 3 of 3)

- [Open Player Modeling: Empowering Players through Data Transparency](https://mlsanity.com/paper/2110.05810/) (2021)
- [AI for Games in the Foundation Model Era](https://mlsanity.com/paper/2609.16679/) (2026)
- [Improving Fairness in Adaptive Social Exergames via Shapley Bandits](https://mlsanity.com/paper/2302.09298/) (2023)

## 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.