# Ludii -- The Ludemic General Game System

arXiv: https://arxiv.org/abs/1905.05013 · HTML: https://mlsanity.com/paper/1905.05013/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2019.

## Built on by (most cited first, 30 of 39)

- [GAVEL: Generating Games Via Evolution and Language Models](https://mlsanity.com/paper/2407.09388/) (2024)
- [General Board Game Concepts](https://mlsanity.com/paper/2107.01078/) (2021)
- [Deep Learning for General Game Playing with Ludii and Polygames](https://mlsanity.com/paper/2101.09562/) (2021)
- [General Game Heuristic Prediction Based on Ludeme Descriptions](https://mlsanity.com/paper/2105.12846/) (2021)
- [Grammar-based Game Description Generation using Large Language Models](https://mlsanity.com/paper/2407.17404/) (2024)
- [Transfer of Fully Convolutional Policy-Value Networks Between Games and Game Variants](https://mlsanity.com/paper/2102.12375/) (2021)
- [Measuring Board Game Distance](https://mlsanity.com/paper/2301.03913/) (2023)
- [Ludii Game Logic Guide](https://mlsanity.com/paper/2101.02120/) (2021)
- [Study and improvement of search algorithms in two-players perfect information games](https://mlsanity.com/paper/2505.09639/) (2025)
- [Spatial State-Action Features for General Games](https://mlsanity.com/paper/2201.06401/) (2022)
- [AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time](https://mlsanity.com/paper/2204.13307/) (2022)
- [The Ludii Game Description Language is Universal](https://mlsanity.com/paper/2205.00451/) (2022)
- [Proof Number Based Monte-Carlo Tree Search](https://mlsanity.com/paper/2303.09449/) (2023)
- [Automatic Generation of Board Game Manuals](https://mlsanity.com/paper/2109.09507/) (2021)
- [Final Adaptation Reinforcement Learning for N-Player Games](https://mlsanity.com/paper/2111.14375/) (2021)
- [Split Moves for Monte-Carlo Tree Search](https://mlsanity.com/paper/2112.07761/) (2021)
- [Combining Monte-Carlo Tree Search with Proof-Number Search](https://mlsanity.com/paper/2206.03965/) (2022)
- [PyTAG: Tabletop Games for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2405.18123/) (2024)
- [Towards a Characterisation of Monte-Carlo Tree Search Performance in Different Games](https://mlsanity.com/paper/2406.09242/) (2024)
- [Grammar and Gameplay-aligned RL for Game Description Generation with LLMs](https://mlsanity.com/paper/2503.15783/) (2025)
- [Boardwalk: Towards a Framework for Creating Board Games with LLMs](https://mlsanity.com/paper/2508.16447/) (2025)
- [Optimised Playout Implementations for the Ludii General Game System](https://mlsanity.com/paper/2111.02839/) (2021)
- [General Board Geometry](https://mlsanity.com/paper/2111.11329/) (2021)
- [Learning to Play Stochastic Two-player Perfect-Information Games without Knowledge](https://mlsanity.com/paper/2302.04318/) (2023)
- [PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games](https://mlsanity.com/paper/2307.09905/) (2023)
- [Exploring RL-based LLM Training for Formal Language Tasks with Programmed Rewards](https://mlsanity.com/paper/2410.17126/) (2024)
- [Anytime Sequential Halving in Monte-Carlo Tree Search](https://mlsanity.com/paper/2411.07171/) (2024)
- [Towards Explaining Monte-Carlo Tree Search by Using Its Enhancements](https://mlsanity.com/paper/2506.13223/) (2025)
- [Best Agent Identification for General Game Playing](https://mlsanity.com/paper/2507.00451/) (2025)
- [CogniPlay: a work-in-progress Human-like model for General Game Playing](https://mlsanity.com/paper/2507.05868/) (2025)

## Abstract (arXiv)

While current General Game Playing (GGP) systems facilitate useful research in Artificial Intelligence (AI) for game-playing, they are often somewhat specialised and computationally inefficient. In this paper, we describe the "ludemic" general game system Ludii, which has the potential to provide an efficient tool for AI researchers as well as game designers, historians, educators and practitioners in related fields. Ludii defines games as structures of ludemes -- high-level, easily understandable game concepts -- which allows for concise and human-understandable game descriptions. We formally describe Ludii and outline its main benefits: generality, extensibility, understandability and efficiency. Experimentally, Ludii outperforms one of the most efficient Game Description Language (GDL) reasoners, based on a propositional network, in all games available in the Tiltyard GGP repository. Moreover, Ludii is also competitive in terms of performance with the more recently proposed Regular Boardgames (RBG) system, and has various advantages in qualitative aspects such as generality.