# Summarizing Strategy Card Game AI Competition

arXiv: https://arxiv.org/abs/2305.11814 · HTML: https://mlsanity.com/paper/2305.11814/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 19 May 2023.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Mapping Hearthstone Deck Spaces through MAP-Elites with Sliding Boundaries](https://mlsanity.com/paper/1904.10656/) (2019)
- [Deep Surrogate Assisted MAP-Elites for Automated Hearthstone Deckbuilding](https://mlsanity.com/paper/2112.03534/) (2021)
- [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)
- [Introducing the Hearthstone-AI Competition](https://mlsanity.com/paper/1906.04238/) (2019)
- [Mastering Strategy Card Game (Legends of Code and Magic) via End-to-End Policy and Optimistic Smooth Fictitious Play](https://mlsanity.com/paper/2303.04096/) (2023)
- [Mastering Strategy Card Game (Hearthstone) with Improved Techniques](https://mlsanity.com/paper/2303.05197/) (2023)
- [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)
- [Evolving Evaluation Functions for Collectible Card Game AI](https://mlsanity.com/paper/2105.01115/) (2021)
- [Helping AI to Play Hearthstone: AAIA'17 Data Mining Challenge](https://mlsanity.com/paper/1708.00730/) (2017)
- [Rolling Horizon Coevolutionary Planning for Two-Player Video Games](https://mlsanity.com/paper/1607.01730/) (2016)
- [Introducing Tales of Tribute AI Competition](https://mlsanity.com/paper/2305.08234/) (2023)

## Built on by (most cited first, 7 of 7)

- [Large Language Models as Agents in Two-Player Games](https://mlsanity.com/paper/2402.08078/) (2024)
- [Learning With Generalised Card Representations for "Magic: The Gathering"](https://mlsanity.com/paper/2407.05879/) (2024)
- [UrzaGPT: LoRA-Tuned Large Language Models for Card Selection in Collectible Card Games](https://mlsanity.com/paper/2508.08382/) (2025)
- [Learning to Beat ByteRL: Exploitability of Collectible Card Game Agents](https://mlsanity.com/paper/2404.16689/) (2024)
- [Causal Reinforcement Learning for Complex Card Games: A Magic The Gathering Benchmark](https://mlsanity.com/paper/2605.06066/) (2026)
- [A Taxonomy of Collectible Card Games from a Game-Playing AI Perspective](https://mlsanity.com/paper/2410.06299/) (2024)
- [Unsound Search with Policy and Value Networks in Legends of Code and Magic](https://mlsanity.com/paper/2609.06816/) (2026)

## Abstract (arXiv)

This paper concludes five years of AI competitions based on Legends of Code and Magic (LOCM), a small Collectible Card Game (CCG), designed with the goal of supporting research and algorithm development. The game was used in a number of events, including Community Contests on the CodinGame platform, and Strategy Card Game AI Competition at the IEEE Congress on Evolutionary Computation and IEEE Conference on Games. LOCM has been used in a number of publications related to areas such as game tree search algorithms, neural networks, evaluation functions, and CCG deckbuilding. We present the rules of the game, the history of organized competitions, and a listing of the participant and their approaches, as well as some general advice on organizing AI competitions for the research community. Although the COG 2022 edition was announced to be the last one, the game remains available and can be played using an online leaderboard arena.