Mech-Elites: Illuminating the Mechanic Space of GVGAI

This paper introduces a fully automatic method of mechanic illumination for general video game level generation. Using the Constrained MAP-Elites algorithm and the GVG-AI framework, this system generates the simplest tile based levels that contain specific sets of game mechanics and also satisfy playability constraints. We apply this method to illuminate mechanic space for $4$ different games in GVG-AI: Zelda, Solarfox, Plants, and RealPortals.

Comparing multimodal optimization and illumination | Proceedings of the Genetic and Evolutionary Computation Conference Companion

Discovering representations for black-box optimization | Proceedings of the 2020 Genetic and Evolutionary Computation Conference

PlayMapper: Illuminating Design Spaces of Platform Games

In this paper, we present PlayMapper, a novel variant of the MAP-Elites algorithm that has been adapted to map the level design space of the Super Mario Bros game. Our approach uses player and level based features to create a map of playable levels. We conduct an experiment to compare the effect of different sets of input features on the range of levels generated using this technique. In this work, we show that existing search-based techniques for PCG can be improved to allow for more control and creative freedom for designers. Current limitations of the system and directions for future work are also discussed.

Innovation Engines | Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

Interactive Constrained MAP-Elites: Analysis and Evaluation of the Expressiveness of the Feature Dimensions

We propose the Interactive Constrained MAP-Elites, a quality-diversity solution for game content generation, implemented as a new feature of the Evolutionary Dungeon Designer: a mixed-initiative co-creativity tool for designing dungeons. The feature uses the MAP-Elites algorithm, an illumination algorithm that segregates the population among several cells depending on their scores with respect to different behavioral dimensions. Users can flexibly and dynamically alternate between these dimensions anytime, thus guiding the evolutionary process in an intuitive way, and then incorporate suggestions produced by the algorithm in their room designs. At the same time, any modifications performed by the human user will feed back into MAP-Elites, closing a circular workflow of constant mutual inspiration. This paper presents the algorithm followed by an in-depth analysis of its behaviour, with the aims of evaluating the expressive range of all possible dimension combinations in several scenarios, as well as discussing their influence in the fitness landscape and in the overall performance of the mixed-initiative procedural content generation.

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.