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.

翻译：在人工智能子领域（如论证、调解与谈判）中，如何寻求主体提案间的妥协方案是一项基础性挑战。遵循这一研究传统，Elkind等人（2021）提出了一种寻求多数支持的提案（相较于现状更优）的联盟构建过程，该过程采用度量空间，其中每个主体具有其理想点。该流程的关键步骤在于识别各主体联盟能够围绕其形成共识的妥协提案。如何有效寻找此类妥协方案仍是一个开放性问题。为填补这一空白，我们通过形式化建模引入主体有限理性与不确定性因素，并开发了生成妥协方案的AI方法。我们聚焦于协作文档编写领域，例如社区章程的民主起草。该方法利用自然语言处理技术与大语言模型，构建文本的语义度量空间。基于该空间，我们设计了用于推荐可能获得广泛支持的妥协点的算法。为评估方法有效性，我们模拟了联盟构建过程，结果表明AI能够促进传统工具受限的大规模民主化文本编辑。