Mortar：自动游戏设计中的机制演化
Mortar: Evolving Mechanics for Automatic Game Design
AI总结：
Mortar通过结合质量-多样性算法和大语言模型，自主演化游戏机制，以生成多样且可玩的游戏，并提升玩家技能排序的得分。
摘要：
我们介绍了Mortar，一个用于自主演化游戏机制以实现自动游戏设计的系统。游戏机制定义了控制游戏玩法的规则和互动，手动设计这些机制是一个耗时且需要专家参与的过程。Mortar结合了质量-多样性算法和大型语言模型，以探索多样化的机制集，这些机制通过合成完整的游戏来评估，这些游戏结合了演化出的机制和来自档案中的机制。机制通过树搜索过程组成完整的游戏进行评估，其中生成的游戏通过其在玩家之间保持基于技能的顺序能力进行评估，即更强的玩家是否始终能战胜较弱的玩家。我们基于机制对游戏技能顺序得分的贡献来评估机制。我们证明Mortar能够生成看起来多样且可玩的游戏，以及对游戏技能顺序得分贡献更大的机制。我们进行了消融研究以评估每个系统组件的作用，并进行用户研究以基于人类反馈评估游戏。
英文摘要：
We present Mortar, a system for autonomously evolving game mechanics for automatic game design. Game mechanics define the rules and interactions that govern gameplay, and designing them manually is a time-consuming and expert-driven process. Mortar combines a quality-diversity algorithm with a large language model to explore a diverse set of mechanics, which are evaluated by synthesising complete games that incorporate both evolved mechanics and those drawn from an archive. The mechanics are evaluated by composing complete games through a tree search procedure, where the resulting games are evaluated by their ability to preserve a skill-based ordering over players -- that is, whether stronger players consistently outperform weaker ones. We assess the mechanics based on their contribution towards the skill-based ordering score in the game. We demonstrate that Mortar produces games that appear diverse and playable, and mechanics that contribute more towards the skill-based ordering score in the game. We perform ablation studies to assess the role of each system component and a user study to evaluate the games based on human feedback.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
