# Mapping Hearthstone Deck Spaces through MAP-Elites with Sliding Boundaries

## TL;DR

This paper introduces MESB, a modified MAP-Elites algorithm with sliding boundaries, applied to Hearthstone to discover diverse strategies and assist in game rebalancing, demonstrating its effectiveness in complex, multidimensional game design spaces.

## Contribution

The paper presents MESB, a novel MAP-Elites variant with sliding boundaries, tailored for complex video game domains like Hearthstone, enabling better exploration of diverse strategies.

## Key findings

- MESB effectively finds diverse high-quality strategies in Hearthstone.
- The algorithm reveals recurring strategic patterns across behavioral dimensions.
- MESB can assist in game rebalancing by exploring varied gameplay styles.

## Abstract

Quality diversity (QD) algorithms such as MAP-Elites have emerged as a powerful alternative to traditional single-objective optimization methods. They were initially applied to evolutionary robotics problems such as locomotion and maze navigation, but have yet to see widespread application. We argue that these algorithms are perfectly suited to the rich domain of video games, which contains many relevant problems with a multitude of successful strategies and often also multiple dimensions along which solutions can vary. This paper introduces a novel modification of the MAP-Elites algorithm called MAP-Elites with Sliding Boundaries (MESB) and applies it to the design and rebalancing of Hearthstone, a popular collectible card game chosen for its number of multidimensional behavior features relevant to particular styles of play. To avoid overpopulating cells with conflated behaviors, MESB slides the boundaries of cells based on the distribution of evolved individuals. Experiments in this paper demonstrate the performance of MESB in Hearthstone. Results suggest MESB finds diverse ways of playing the game well along the selected behavioral dimensions. Further analysis of the evolved strategies reveals common patterns that recur across behavioral dimensions and explores how MESB can help rebalance the game.