用最小化世界表示让大模型预测行动后果，提升角色扮演游戏自由度。
PAYADOR: A Minimalist Approach to Grounding Language Models on Structured Data for Interactive Storytelling and Role-playing Games
- 不预设动作，直接让模型预测玩家行为的结果
- 基于极简世界状态表示，实现稳定且自然的剧情演化
- 适合研究互动叙事与共创式游戏的开发者和研究者
每次互动叙事系统接收玩家输入时，都会面临世界状态更新问题。传统方法将输入映射到预设动作，严重限制玩家自由。在强调即兴创作的角色扮演游戏（RPG）中，此问题尤为关键。本文提出PAYADOR，一种新方法：不建模动作本身，而是预测动作可能带来的结果。通过将大语言模型接地于极简的虚构世界表示，实现了令人满意的动态叙事效果。该工作已开源，可被用于探索RPG中协同创造力的潜力。
原文摘要 · Abstract (English)
Every time an Interactive Storytelling (IS) system gets a player input, it is facing the world-update problem. Classical approaches to this problem consist in mapping that input to known preprogrammed actions, what can severely constrain the free will of the player. When the expected experience has a strong focus on improvisation, like in Role-playing Games (RPGs), this problem is critical. In this paper we present PAYADOR, a different approach that focuses on predicting the outcomes of the actions instead of representing the actions themselves. To implement this approach, we ground a Large Language Model to a minimal representation of the fictional world, obtaining promising results. We make this contribution open-source, so it can be adapted and used for other related research on unleashing the co-creativity power of RPGs.
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