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.
|Titel||Proceedings of the 2020 IEEE Conference on Games (CoG)|
|Status||Udgivet - 2020|
|Begivenhed||IEEE Conference on Games 2020 - |
Varighed: 24 aug. 2020 → 27 nov. 2020
|Konference||IEEE Conference on Games 2020|
|Periode||24/08/2020 → 27/11/2020|
FingeraftrykDyk ned i forskningsemnerne om 'Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error'. Sammen danner de et unikt fingeraftryk.
Pris: Priser, stipendier, udnævnelser