# Evolving Evaluation Functions for Collectible Card Game AI

arXiv: https://arxiv.org/abs/2105.01115 · HTML: https://mlsanity.com/paper/2105.01115/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 39 references that are in the map.
- First posted on arXiv 3 May 2021.

## Builds on (most cited first, 10 of 10)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Mapping Hearthstone Deck Spaces through MAP-Elites with Sliding Boundaries](https://mlsanity.com/paper/1904.10656/) (2019)
- [Improving Hearthstone AI by Combining MCTS and Supervised Learning Algorithms](https://mlsanity.com/paper/1808.04794/) (2018)
- [The Many AI Challenges of Hearthstone](https://mlsanity.com/paper/1907.06562/) (2019)
- [The N-Tuple Bandit Evolutionary Algorithm for Game Agent Optimisation](https://mlsanity.com/paper/1802.05991/) (2018)
- [Evolving the Hearthstone Meta](https://mlsanity.com/paper/1907.01623/) (2019)
- [Evolutionary Approach to Collectible Card Game Arena Deckbuilding using Active Genes](https://mlsanity.com/paper/2001.01326/) (2020)
- [Q-DeckRec: A Fast Deck Recommendation System for Collectible Card Games](https://mlsanity.com/paper/1806.09771/) (2018)
- [Helping AI to Play Hearthstone: AAIA'17 Data Mining Challenge](https://mlsanity.com/paper/1708.00730/) (2017)
- [Genetic Algorithms for Evolving Computer Chess Programs](https://mlsanity.com/paper/1711.08337/) (2017)

## Built on by (most cited first, 6 of 6)

- [Summarizing Strategy Card Game AI Competition](https://mlsanity.com/paper/2305.11814/) (2023)
- [Learning With Generalised Card Representations for "Magic: The Gathering"](https://mlsanity.com/paper/2407.05879/) (2024)
- [Introducing Tales of Tribute AI Competition](https://mlsanity.com/paper/2305.08234/) (2023)
- [Learning to Beat ByteRL: Exploitability of Collectible Card Game Agents](https://mlsanity.com/paper/2404.16689/) (2024)
- [Two-Step Reinforcement Learning for Multistage Strategy Card Game](https://mlsanity.com/paper/2311.17305/) (2023)
- [AI Agents for the Dhumbal Card Game: A Comparative Study](https://mlsanity.com/paper/2510.11736/) (2025)

## Abstract (arXiv)

In this work, we presented a study regarding two important aspects of evolving feature-based game evaluation functions: the choice of genome representation and the choice of opponent used to test the model. We compared three representations. One simpler and more limited, based on a vector of weights that are used in a linear combination of predefined game features. And two more complex, based on binary and n-ary trees. On top of this test, we also investigated the influence of fitness defined as a simulation-based function that: plays against a fixed weak opponent, plays against a fixed strong opponent, and plays against the best individual from the previous population. For a testbed, we have chosen a recently popular domain of digital collectible card games. We encoded our experiments in a programming game, Legends of Code and Magic, used in Strategy Card Game AI Competition. However, as the problems stated are of general nature we are convinced that our observations are applicable in the other domains as well.