GAVEL: Generating Games Via Evolution and Language Models
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.
66948a7b3774cc5e5c437bdd
Graham Todd
66948a7b3774cc5e5c437bde
Alexander Padula
66948a7b3774cc5e5c437bdf
Matthew Stephenson
claimed_verified
2024-08-02T07:35:05.622Z
65b1a3b6b389ca2de1296658
/avatars/a78a9da6f94fc5683aa3184cd5975253.svg
Matthew Stephenson
user
step0330
66948a7b3774cc5e5c437be0
Éric Piette
66948a7b3774cc5e5c437be1
Dennis J. N. J. Soemers
claimed_verified
2024-07-16T20:16:27.311Z
62cb2f860d72a9876be10bd5
https://cdn-avatars.huggingface.co/v1/production/uploads/62cb2f860d72a9876be10bd5/RmY_TViUGrY_2Yh5lGIKF.jpeg
Dennis Soemers
user
DennisSoemers
66948a7b3774cc5e5c437be2
Julian Togelius
GAVEL: Generating Games Via Evolution and Language Models
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.
A novel approach using large language models and evolutionary computation generates new board games in the Ludii language, exploring previously uncovered rule spaces.
Ludii game description language large language models evolutionary computation rule representations game generation