From deciding on a PhD program to buying a new camera, unfamiliar decisions--decisions without domain knowledge--are frequent and significant. The complexity and uncertainty of such decisions demand unique approaches to information seeking, understanding, and decision-making. Our formative study highlights that users want to start by discovering broad and relevant domain information evenly and simultaneously, quickly address emerging inquiries, and gain personalized standards to assess information found. We present ChoiceMates, an interactive multi-agent system designed to address these needs by enabling users to engage with a dynamic set of LLM agents each presenting a unique experience in the domain. Unlike existing multi-agent systems that automate tasks with agents, the user orchestrates agents to assist their decision-making process. Our user evaluation (n=12) shows that ChoiceMates enables a more confident, satisfactory decision-making with better situation understanding than web search, and higher decision quality and confidence than a commercial multi-agent framework. This work provides insights into designing a more controllable and collaborative multi-agent system.

翻译：从选择博士项目到购买新相机，陌生决策——即缺乏领域知识的决策——是频繁且重要的。此类决策的复杂性和不确定性要求采用独特的信息搜寻、理解与决策方法。我们的形成性研究表明，用户希望首先均衡且同步地发现广泛而相关的领域信息，快速解决新出现的疑问，并获得个性化标准以评估所发现的信息。为此，我们提出了ChoiceMates，一个交互式多智能体系统，旨在通过让用户与一组动态的LLM智能体进行交互来满足这些需求，每个智能体都在该领域提供独特的体验。与现有通过智能体自动化任务的多智能体系统不同，在ChoiceMates中，用户协调智能体以辅助其决策过程。我们的用户评估（n=12）表明，与网络搜索相比，ChoiceMates能使用户在决策时更有信心、更满意，并具有更好的情境理解；与商业多智能体框架相比，它能带来更高的决策质量和信心。这项工作为设计更具可控性和协作性的多智能体系统提供了见解。