自私但明智：探究混合动机人机交互中智能体人格的影响
Be Selfish, But Wisely: Investigating the Impact of Agent Personality in Mixed-Motive Human-Agent Interactions
AI总结：
本文发现自我博弈强化学习训练的谈判系统因缺乏妥协导致协议失败，基于经济学理论设计了具备不同人格的智能体，证明在最大化自身利益同时避免谈判破裂的自私智能体表现最优。
摘要：
设计谈判对话系统的一种自然方法是通过自我博弈强化学习（self-play RL）：训练一个智能体，通过与旨在模仿人人对话数据的模拟用户进行交互，来学习最大化其自身性能。尽管这一流程在先前的研究中被采用，我们发现它会导致一个根本性缺陷的系统，该系统无法学会在谈判中妥协的价值，这常常导致无法达成协议（即谈判伙伴未达成交易就离开），最终损害了模型的整体性能。我们在 DealOrNoDeal 任务的背景下调查了这一现象，这是一个关于书籍、帽子和球的多议题谈判。基于经济学的谈判理论，我们以两种新颖的方式修改了训练流程，以设计具有多样化人格的智能体，并分析了它们与人类伙伴合作时的性能。我们发现，尽管这两种技术都显示出了潜力，但一个自私的智能体——在最大化自身性能的同时也避免谈判伙伴离开——通过隐式地学会为自身和谈判伙伴创造价值，表现优于其他变体。我们讨论了我们的发现对于成功的谈判对话系统的意义，以及未来应如何设计这些系统。
英文摘要：
A natural way to design a negotiation dialogue system is via self-play RL: train an agent that learns to maximize its performance by interacting with a simulated user that has been designed to imitate human-human dialogue data. Although this procedure has been adopted in prior work, we find that it results in a fundamentally flawed system that fails to learn the value of compromise in a negotiation, which can often lead to no agreements (i.e., the partner walking away without a deal), ultimately hurting the model's overall performance. We investigate this observation in the context of the DealOrNoDeal task, a multi-issue negotiation over books, hats, and balls. Grounded in negotiation theory from Economics, we modify the training procedure in two novel ways to design agents with diverse personalities and analyze their performance with human partners. We find that although both techniques show promise, a selfish agent, which maximizes its own performance while also avoiding walkaways, performs superior to other variants by implicitly learning to generate value for both itself and the negotiation partner. We discuss the implications of our findings for what it means to be a successful negotiation dialogue system and how these systems should be designed in the future.
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。
