In the last decade, conversational search has attracted considerable attention. However, most research has focused on systems that can support a \emph{single} searcher. In this paper, we explore how systems can support \emph{multiple} searchers collaborating over an instant messaging platform (i.e., Slack). We present a ``Wizard of Oz'' study in which 27 participant pairs collaborated on three information-seeking tasks over Slack. Participants were unable to search on their own and had to gather information by interacting with a \emph{searchbot} directly from the Slack channel. The role of the searchbot was played by a reference librarian. Conversational search systems must be capable of engaging in \emph{mixed-initiative} interaction by taking and relinquishing control of the conversation to fulfill different objectives. Discourse analysis research suggests that conversational agents can take \emph{two} levels of initiative: dialog- and task-level initiative. Agents take dialog-level initiative to establish mutual belief between agents and task-level initiative to influence the goals of the other agents. During the study, participants were exposed to three experimental conditions in which the searchbot could take different levels of initiative: (1) no initiative, (2) only dialog-level initiative, and (3) both dialog- and task-level initiative. In this paper, we focus on understanding the Wizard's actions. Specifically, we focus on understanding the Wizard's motivations for taking initiative and their rationale for the timing of each intervention. To gain insights about the Wizard's actions, we conducted a stimulated recall interview with the Wizard. We present findings from a qualitative analysis of this interview data and discuss implications for designing conversational search systems to support collaborative search.

翻译：过去十年间，对话式搜索引起了广泛关注。然而，多数研究聚焦于支持单个搜索者的系统。本文探索如何设计系统支持多个搜索者通过即时通讯平台（即Slack）进行协作。我们开展了一项"巫师之幕"研究，27组参与者通过Slack协作完成三项信息搜索任务。参与者无法自行搜索，必须通过与Slack频道中的搜索机器人直接交互来获取信息。该搜索机器人由一名参考图书馆员扮演。对话式搜索系统必须能够通过获取和释放对话控制权来实现混合主动性交互，以满足不同目标。话语分析研究表明，对话代理可展现两种主动性层级：对话级主动性和任务级主动性。代理通过对话级主动性建立与用户的共同信念，通过任务级主动性影响其他代理的目标。研究中，参与者体验了三种实验条件：搜索机器人分别呈现（1）无主动性、（2）仅对话级主动性、（3）同时具备对话级与任务级主动性。本研究的重点在于理解巫师的行为，具体包括其采取主动性的动机与干预时机的决策依据。为深入探究巫师的行动逻辑，我们对其进行了刺激回忆访谈。通过对访谈数据的定性分析，我们揭示了相关发现，并讨论了设计支持协同搜索的对话式搜索系统的启示。