用公平性设计谈判策略，让AI更懂人类合作
A Fairness-Driven Method for Learning Human-Compatible Negotiation Strategies
- 将公平性融入奖励与搜索，引导AI学习人类兼容的谈判方式
- 在多轮谈判中实现更平等的结果，提升整体协商质量
- 适合希望AI更人性化、减少对抗性的研究与应用
尽管人工智能与自然语言处理取得进展，谈判仍是AI难以攻克的领域。传统博弈论方法在零和游戏中表现良好，但在谈判中因无法学习人类兼容策略而受限；仅依赖人类数据的方法则存在领域局限且缺乏理论保障。本文提出一种名为FDHC的谈判框架，以公平性作为一般和博弈中的最优标准，将公平性同时纳入奖励设计与搜索过程，以学习人类兼容的谈判策略。该方法引入一种新型强化学习+搜索技术LGM-Zero，利用预训练语言模型从大规模动作空间中检索出人类兼容的提议。实验结果表明，该方法能够实现更平等的谈判结果，并显著提升协商质量。
原文摘要 · Abstract (English)
Despite recent advancements in AI and NLP, negotiation remains a difficult domain for AI agents. Traditional game theoretic approaches that have worked well for two-player zero-sum games struggle in the context of negotiation due to their inability to learn human-compatible strategies. On the other hand, approaches that only use human data tend to be domain-specific and lack the theoretical guarantees provided by strategies grounded in game theory. Motivated by the notion of fairness as a criterion for optimality in general sum games, we propose a negotiation framework called FDHC which incorporates fairness into both the reward design and search to learn human-compatible negotiation strategies. Our method includes a novel, RL+search technique called LGM-Zero which leverages a pre-trained language model to retrieve human-compatible offers from large action spaces. Our results show that our method is able to achieve more egalitarian negotiation outcomes and improve negotiation quality.
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