# Experience Management in Multi-player Games

arXiv: https://arxiv.org/abs/1907.02349 · HTML: https://mlsanity.com/paper/1907.02349/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2019.

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

- [Player Modeling via Multi-Armed Bandits](https://mlsanity.com/paper/2102.05264/) (2021)
- [Player-Centered AI for Automatic Game Personalization: Open Problems](https://mlsanity.com/paper/2102.07548/) (2021)
- [Improving Fairness in Adaptive Social Exergames via Shapley Bandits](https://mlsanity.com/paper/2302.09298/) (2023)

## Abstract (arXiv)

Experience Management studies AI systems that automatically adapt interactive experiences such as games to tailor to specific players and to fulfill design goals. Although it has been explored for several decades, existing work in experience management has mostly focused on single-player experiences. This paper is a first attempt at identifying the main challenges to expand EM to multi-player/multi-user games or experiences. We also make connections to related areas where solutions for similar problems have been proposed (especially group recommender systems) and discusses the potential impact and applications of multi-player EM.