title: GAVEL: Generating Games Via Evolution and Language Models
summary: Automatically generating novel and interesting games is a complex task.
  Challenges include representing game rules in a computationally workable form,
  searching through the large space of potential games under most such
  representations, and accurately evaluating the originality and quality of
  previously unseen games. Prior work in automated game generation has largely
  focused on relatively restricted rule representations and relied on
  domain-specific heuristics. In this work, we explore the generation of novel
  games in the comparatively expansive Ludii game description language, which
  encodes the rules of over 1000 board games in a variety of styles and modes of
  play. We draw inspiration from recent advances in large language models and
  evolutionary computation in order to train a model that intelligently mutates
  and recombines games and mechanics expressed as code. We demonstrate both
  quantitatively and qualitatively that our approach is capable of generating new
  and interesting games, including in regions of the potential rules space not
  covered by existing games in the Ludii dataset. A sample of the generated games
  are available to play online through the Ludii portal.
paper_authors._id: 66948a7b3774cc5e5c437bdd
paper_authors.name: Graham Todd
paper_authors._id: 66948a7b3774cc5e5c437bde
paper_authors.name: Alexander Padula
paper_authors._id: 66948a7b3774cc5e5c437bdf
paper_authors.name: Matthew Stephenson
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-02T07:35:05.622Z
paper_authors._id: 65b1a3b6b389ca2de1296658
paper_authors.avatarUrl: /avatars/a78a9da6f94fc5683aa3184cd5975253.svg
paper_authors.fullname: Matthew Stephenson
paper_authors.type: user
paper_authors.user: step0330
paper_authors._id: 66948a7b3774cc5e5c437be0
paper_authors.name: Éric Piette
paper_authors._id: 66948a7b3774cc5e5c437be1
paper_authors.name: Dennis J. N. J. Soemers
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-16T20:16:27.311Z
paper_authors._id: 62cb2f860d72a9876be10bd5
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1657483110003-noauth.jpeg
paper_authors.fullname: Dennis Soemers
paper_authors.type: user
paper_authors.user: DennisSoemers
paper_authors._id: 66948a7b3774cc5e5c437be2
paper_authors.name: Julian Togelius
paper_title: GAVEL: Generating Games Via Evolution and Language Models
paper_summary: Automatically generating novel and interesting games is a complex task.
  Challenges include representing game rules in a computationally workable form,
  searching through the large space of potential games under most such
  representations, and accurately evaluating the originality and quality of
  previously unseen games. Prior work in automated game generation has largely
  focused on relatively restricted rule representations and relied on
  domain-specific heuristics. In this work, we explore the generation of novel
  games in the comparatively expansive Ludii game description language, which
  encodes the rules of over 1000 board games in a variety of styles and modes of
  play. We draw inspiration from recent advances in large language models and
  evolutionary computation in order to train a model that intelligently mutates
  and recombines games and mechanics expressed as code. We demonstrate both
  quantitatively and qualitatively that our approach is capable of generating new
  and interesting games, including in regions of the potential rules space not
  covered by existing games in the Ludii dataset. A sample of the generated games
  are available to play online through the Ludii portal.
paper_ai_keywords: Ludii game description language large language models evolutionary computation game-rule domain-specific heuristics game mechanics encoder styles modes of play potential rules space Ludii portal