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Ludii -- The Ludemic General Game System

arXiv 1905.05013 ↗First posted 13 May 2019cs.AIcited by 39

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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.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 May 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 10; 2022: 5; 2023: 5; 2024: 7; 2025: 10; 2026: 2.

39 papers.

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  1. 2024GAVEL: Generating Games Via Evolution and Language Modelscited by 24
  2. 2021General Board Game Conceptscited by 8
  3. 2021Deep Learning for General Game Playing with Ludii and Polygamescited by 7
  4. 2021General Game Heuristic Prediction Based on Ludeme Descriptionscited by 7
  5. 2024Grammar-based Game Description Generation using Large Language Modelscited by 7
  6. 2021Transfer of Fully Convolutional Policy-Value Networks Between Games and Game Variantscited by 6
  7. 2023Measuring Board Game Distancecited by 5
  8. 2021Ludii Game Logic Guidecited by 4
  9. 2025Study and improvement of search algorithms in two-players perfect information gamescited by 4
  10. 2022Spatial State-Action Features for General Gamescited by 3
  11. 2022AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Timecited by 3
  12. 2022The Ludii Game Description Language is Universalcited by 3
27 more
  1. 2023Proof Number Based Monte-Carlo Tree Searchcited by 3
  2. 2021Automatic Generation of Board Game Manualscited by 2
  3. 2021Final Adaptation Reinforcement Learning for N-Player Gamescited by 2
  4. 2021Split Moves for Monte-Carlo Tree Searchcited by 2
  5. 2022Combining Monte-Carlo Tree Search with Proof-Number Searchcited by 2
  6. 2024Towards a Characterisation of Monte-Carlo Tree Search Performance in Different Gamescited by 2
  7. 2025Grammar and Gameplay-aligned RL for Game Description Generation with LLMscited by 2
  8. 2025Boardwalk: Towards a Framework for Creating Board Games with LLMscited by 2
  9. 2021Optimised Playout Implementations for the Ludii General Game Systemcited by 1
  10. 2021General Board Geometrycited by 1
  11. 2023Learning to Play Stochastic Two-player Perfect-Information Games without Knowledgecited by 1
  12. 2023PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Gamescited by 1
  13. 2024PyTAG: Tabletop Games for Multi-Agent Reinforcement Learningcited by 1
  14. 2024Exploring RL-based LLM Training for Formal Language Tasks with Programmed Rewardscited by 1
  15. 2024Anytime Sequential Halving in Monte-Carlo Tree Searchcited by 1
  16. 2025Best Agent Identification for General Game Playingcited by 1
  17. 2025CogniPlay: a work-in-progress Human-like model for General Game Playingcited by 1
  18. 2025JSON-Bag: A generic game trajectory representationcited by 1
  19. 2022A Technique to Create Weaker Abstract Board Game Agents via Reinforcement Learningcited by 0
  20. 2023Hybrid Minimax-MCTS and Difficulty Adjustment for General Game Playingcited by 0
  21. 2024A Research Agenda for Usability and Generalisation in Reinforcement Learningcited by 0
  22. 2025Towards Explaining Monte-Carlo Tree Search by Using Its Enhancementscited by 0
  23. 2025Generalized Proof-Number Monte-Carlo Tree Searchcited by 0
  24. 2025Ludax: A GPU-Accelerated Domain Specific Language for Board Gamescited by 0
  25. 2025Usando LLMs para Programar Jogos de Tabuleiro e Variacc~oescited by 0
  26. 2026Regret-Guided Search Control for Efficient Learning in AlphaZerocited by 0
  27. 2026Generalized Rapid Action Value Estimation in Memory-Constrained Environmentscited by 0