Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error

• URL: http://arxiv.org/abs/2005.07677v2
• Date: Thu, 25 Jun 2020 12:55:08 GMT
• Title: Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error
• Authors: Miguel Gonz\'alez-Duque, Rasmus Berg Palm, David Ha, Sebastian Risi
• Abstract summary: Methods for dynamic difficulty adjustment allow games to be tailored to particular players to maximize their engagement. Current methods often only modify a limited set of game features such as the difficulty of the opponents, or the availability of resources. This paper presents a method that can generate and search for complete levels with a specific target difficulty in only a few trials.
• Score: 16.297059109611798
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Methods for dynamic difficulty adjustment allow games to be tailored to particular players to maximize their engagement. However, current methods often only modify a limited set of game features such as the difficulty of the opponents, or the availability of resources. Other approaches, such as experience-driven Procedural Content Generation (PCG), can generate complete levels with desired properties such as levels that are neither too hard nor too easy, but require many iterations. This paper presents a method that can generate and search for complete levels with a specific target difficulty in only a few trials. This advance is enabled by through an Intelligent Trial-and-Error algorithm, originally developed to allow robots to adapt quickly. Our algorithm first creates a large variety of different levels that vary across predefined dimensions such as leniency or map coverage. The performance of an AI playing agent on these maps gives a proxy for how difficult the level would be for another AI agent (e.g. one that employs Monte Carlo Tree Search instead of Greedy Tree Search); using this information, a Bayesian Optimization procedure is deployed, updating the difficulty of the prior map to reflect the ability of the agent. The approach can reliably find levels with a specific target difficulty for a variety of planning agents in only a few trials, while maintaining an understanding of their skill landscape.

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