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

arXiv: https://arxiv.org/abs/1904.10656 · HTML: https://mlsanity.com/paper/1904.10656/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2019.

## Built on by (most cited first, 30 of 32)

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- [Dynamics-Aware Quality-Diversity for Efficient Learning of Skill Repertoires](https://mlsanity.com/paper/2109.08522/) (2021)
- [Learning Controllable 3D Level Generators](https://mlsanity.com/paper/2206.13623/) (2022)
- [Multi-Robot Coordination and Layout Design for Automated Warehousing](https://mlsanity.com/paper/2305.06436/) (2023)
- [Accelerated Quality-Diversity through Massive Parallelism](https://mlsanity.com/paper/2202.01258/) (2022)
- [Games for Artificial Intelligence Research: A Review and Perspectives](https://mlsanity.com/paper/2304.13269/) (2023)
- [Training Diverse High-Dimensional Controllers by Scaling Covariance Matrix Adaptation MAP-Annealing](https://mlsanity.com/paper/2210.02622/) (2022)
- [Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning](https://mlsanity.com/paper/2211.02193/) (2022)
- [Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven Optimization](https://mlsanity.com/paper/2310.12103/) (2023)
- [Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML](https://mlsanity.com/paper/2307.08364/) (2023)
- [Learning to Walk Autonomously via Reset-Free Quality-Diversity](https://mlsanity.com/paper/2204.03655/) (2022)
- [Quality Diversity Evolutionary Learning of Decision Trees](https://mlsanity.com/paper/2208.12758/) (2022)
- [Summarizing Strategy Card Game AI Competition](https://mlsanity.com/paper/2305.11814/) (2023)
- [Evolving Evaluation Functions for Collectible Card Game AI](https://mlsanity.com/paper/2105.01115/) (2021)
- [Measuring Diversity of Game Scenarios](https://mlsanity.com/paper/2404.15192/) (2024)
- [Destination-to-Chutes Task Mapping Optimization for Multi-Robot Coordination in Robotic Sorting Systems](https://mlsanity.com/paper/2510.03472/) (2025)
- [Introducing Tales of Tribute AI Competition](https://mlsanity.com/paper/2305.08234/) (2023)
- [Multi-Objective Quality-Diversity in Unstructured and Unbounded Spaces](https://mlsanity.com/paper/2504.03715/) (2025)
- [Persona-driven Dominant/Submissive Map (PDSM) Generation for Tutorials](https://mlsanity.com/paper/2204.05217/) (2022)
- [Soft Quality-Diversity Optimization](https://mlsanity.com/paper/2512.00810/) (2025)
- [Optimization of Edge Directions and Weights for Mixed Guidance Graphs in Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2602.23468/) (2026)
- [HAPEns: Hardware-Aware Post-Hoc Ensembling for Tabular Data](https://mlsanity.com/paper/2603.10582/) (2026)
- [Rinascimento: searching the behaviour space of Splendor](https://mlsanity.com/paper/2106.08371/) (2021)
- [Automated Graph Genetic Algorithm based Puzzle Validation for Faster Game Design](https://mlsanity.com/paper/2302.09040/) (2023)
- [From Metrics to Meaning: Time to Rethink Evaluation in Human-AI Collaborative Design](https://mlsanity.com/paper/2402.07911/) (2024)
- [Dynamic Quality-Diversity Search](https://mlsanity.com/paper/2404.05769/) (2024)
- [Identifying and Clustering Counter Relationships of Team Compositions in PvP Games for Efficient Balance Analysis](https://mlsanity.com/paper/2408.17180/) (2024)

## Abstract (arXiv)

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.