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.
|Title of host publication||Proceedings of the 2020 IEEE Conference on Games (CoG)|
|Number of pages||8|
|Publication status||Published - 2020|
|Event||IEEE Conference on Games 2020 - |
Duration: 24 Aug 2020 → 27 Nov 2020
|Conference||IEEE Conference on Games 2020|
|Period||24/08/2020 → 27/11/2020|
- Dynamic Difficulty Adjustment
- Intelligent Trial-and-Error
- Planning Agents
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