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Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error
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.Comment: To be presented in the Conference on Games 202
The experience-driven perspective
Ultimately, content is generated for the player. But so far, our algorithms
have not taken specific players into account. Creating computational models of a
player’s behaviour, preferences, or skills is called player modelling. With a model
of the player, we can create algorithms that create content specifically tailored to
that player. The experience-driven perspective on procedural content generation provides
a framework for content generation based on player modelling; one of the most
important ways of doing this is to use a player model in the evaluation function for
search-based PCG. This chapter discusses different ways of collecting and encoding
data about the player, primarily player experience, and ways of modelling this data.
It also gives examples of different ways in which such models can be used.peer-reviewe
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