GAVEL：透過進化和語言模型生成遊戲

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

自動生成新穎且有趣的遊戲是一項複雜的任務。挑戰包括以可計算的形式表示遊戲規則、在大量潛在遊戲空間中搜索，並準確評估以前未見遊戲的獨創性和質量。自動遊戲生成的先前工作主要集中在相對受限制的規則表示上，並依賴於特定領域的經驗法則。在這項研究中，我們探索在相對豐富的Ludii遊戲描述語言中生成新穎遊戲，該語言編碼了1000多種棋盤遊戲的規則，並具有各種風格和遊戲模式。我們從最近在大型語言模型和進化計算方面的進展中汲取靈感，以訓練一個能夠智能地變異和重組遊戲和機制的模型，這些遊戲和機制以代碼形式表達。我們定量和定性地證明，我們的方法能夠生成新穎且有趣的遊戲，包括Ludii數據集中現有遊戲未涵蓋的潛在規則空間區域。一些生成的遊戲樣本可通過Ludii門戶網站在線遊玩。

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.