GAVEL：通过进化和语言模型生成游戏

GAVEL: Generating Games Via Evolution and Language Models

July 12, 2024

作者: Graham Todd, Alexander Padula, Matthew Stephenson, Éric Piette, Dennis J. N. J. Soemers, Julian Togelius

cs.AI

摘要

自动生成新颖有趣的游戏是一项复杂的任务。挑战包括以计算上可行的形式表示游戏规则，搜索在大多数这种表示下潜在游戏空间中的游戏，并准确评估以前未见游戏的独创性和质量。自动生成游戏的先前工作主要集中在相对受限制的规则表示上，并依赖于特定领域的启发式方法。在这项工作中，我们探讨了在相对广泛的Ludii游戏描述语言中生成新颖游戏的方法，该语言编码了1000多种棋盘游戏的规则，以各种风格和玩法模式。我们从最近在大型语言模型和进化计算方面取得的进展中汲取灵感，以训练一个能够智能地变异和重组以代码形式表达的游戏和机制的模型。我们定量和定性地证明，我们的方法能够生成新颖有趣的游戏，包括在Ludii数据集中现有游戏未涵盖的潜在规则空间中。一些生成的游戏样本可通过Ludii门户网站在线游玩。

English

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.