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Multiplayer Modeling via Multi-Armed Bandits
Conference proceeding   Open access

Multiplayer Modeling via Multi-Armed Bandits

Robert C Gray, Jichen Zhu and Santiago Ontanon
2021 IEEE Conference on Games (CoG), pp 01-08
17 Aug 2021
DOI: https://doi.org/10.1109/CoG52621.2021.9618892
url
https://pure.itu.dk/portal/da/publications/3d0b8172-b483-4d63-b436-2a512822d4cdView
Submitted Open Open Access (License Unspecified)

Abstract

Adaptation models Artificial intelligence Conferences Estimation Games History linear regression machine learning multi-armed bandit multiplayer modeling Predictive models reinforcement learning
This paper focuses on player modeling in multiplayer adaptive games. While player modeling has received a significant amount of attention, less is known about how to use player modeling in multiplayer games, especially when an experience management AI must make decisions on how to adapt the experience for the group as a whole. Specifically, we present a multi-armed bandit (MAB) approach for modeling groups of multiple players. Our main contributions are a new MAB framework for multiplayer modeling and techniques for addressing the new challenges introduced by the multiplayer context, extending previous work on MAB-based player modeling to account for new group-generated phenomena not present in single-user models. We evaluate our approach via simulation of virtual players in the context of multiplayer adaptive exergames.

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10 citations in Scopus

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