标题： 人工智能生成的联盟形成折衷方案 显示英文标题

标题： AI-Generated Compromises for Coalition Formation

摘要： 在论证、调解和谈判等人工智能子领域中，寻找代理人提议之间的折衷方案是一项根本性挑战。 在此传统基础上，Elkind 等人（2021）提出了一种联盟形成过程，该过程寻求优于现状的多数支持提案，并使用了一个度量空间，其中每个代理人都有一个理想点。 这一过程中一个关键步骤涉及识别折衷提案，以使代理人的联盟能够团结一致。 如何有效地找到这样的折衷提案仍然是一个开放的问题。 我们通过形式化一种结合了代理有限理性和不确定性的模型，并开发人工智能方法来生成折衷提案来填补这一空白。 我们专注于协作文档撰写领域，例如社区宪法的民主起草。 我们的方法使用自然语言处理技术和大型语言模型来诱导文本上的语义度量空间。 基于此空间，我们设计算法以建议可能获得广泛支持的折衷点。 为了评估我们的方法，我们模拟了联盟形成过程，并表明人工智能可以促进大规模民主文本编辑，这是传统工具受限的领域。 显示英文摘要

摘要： The challenge of finding compromises between agent proposals is fundamental to AI subfields such as argumentation, mediation, and negotiation. Building on this tradition, Elkind et al. (2021) introduced a process for coalition formation that seeks majority-supported proposals preferable to the status quo, using a metric space where each agent has an ideal point. A crucial step in this process involves identifying compromise proposals around which agent coalitions can unite. How to effectively find such compromise proposals remains an open question. We address this gap by formalizing a model that incorporates agent bounded rationality and uncertainty, and by developing AI methods to generate compromise proposals. We focus on the domain of collaborative document writing, such as the democratic drafting of a community constitution. Our approach uses natural language processing techniques and large language models to induce a semantic metric space over text. Based on this space, we design algorithms to suggest compromise points likely to receive broad support. To evaluate our methods, we simulate coalition formation processes and show that AI can facilitate large-scale democratic text editing, a domain where traditional tools are limited.