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

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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.
Original languageEnglish
Title of host publicationProceedings of the 2020 IEEE Conference on Games (CoG)
Number of pages8
Publication date2020
Publication statusPublished - 2020
EventIEEE Conference on Games 2020 -
Duration: 24 Aug 202027 Nov 2020


ConferenceIEEE Conference on Games 2020

    Research areas

  • Dynamic Difficulty Adjustment, Intelligent Trial-and-Error, Planning Agents, PCG, MAP-Elites


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ID: 85520072