GAIG Game AI Research Group @ QMUL

Modeling Player Experience with the N-Tuple Bandit Evolutionary Algorithm

2018
Kunanusont, Kamolwan and Lucas, Simon Mark and Perez-Liebana, Diego

Abstract

Automatic game design is an increasingly popular area of research that consists of devising systems that create content or complete games autonomously. The interest in such systems is two-fold: games can be highly stochastic environments that allow presenting this task as a complex optimization problem and automatic play-testing, becoming benchmarks to advance the state of the art on AI methods. In this paper, we propose a general approach that employs the N-Tuple Bandit Evolutionary Algorithm (NTBEA) to tune parameters of three different games of the General Video Game AI (GVGAI) framework. The objective is to adjust the game experience of the players so the distribution of score events through the game approximates certain pre-defined target curves. We report satisfactory results for different target score trends and games, paving the path for future research in the area of automatically tuning player experience.

Cite this work

@inproceedings{gaina2018vertigo,
author= {Kunanusont, Kamolwan and Lucas, Simon Mark and Perez-Liebana, Diego},
title= {{Modeling Player Experience with the N-Tuple Bandit Evolutionary Algorithm}},
year= {2018},
booktitle= {{The 14th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment}},
abstract= {Automatic game design is an increasingly popular area of research that consists of devising systems that create content or complete games autonomously. The interest in such systems is two-fold: games can be highly stochastic environments that allow presenting this task as a complex optimization problem and automatic play-testing, becoming benchmarks to advance the state of the art on AI methods. In this paper, we propose a general approach that employs the N-Tuple Bandit Evolutionary Algorithm (NTBEA) to tune parameters of three different games of the General Video Game AI (GVGAI) framework. The objective is to adjust the game experience of the players so the distribution of score events through the game approximates certain pre-defined target curves. We report satisfactory results for different target score trends and games, paving the path for future research in the area of automatically tuning player experience.},
}

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