The concept of a Human-AI team has gained increasing attention in recent years. For effective collaboration between humans and AI teammates, proactivity is crucial for close coordination and effective communication. However, the design of adequate proactivity for AI-based systems to support humans is still an open question and a challenging topic. In this paper, we present the development of a corpus-based user simulator for training and testing proactive dialog policies. The simulator incorporates informed knowledge about proactive dialog and its effect on user trust and simulates user behavior and personal information, including socio-demographic features and personality traits. Two different simulation approaches were compared, and a task-step-based approach yielded better overall results due to enhanced modeling of sequential dependencies. This research presents a promising avenue for exploring and evaluating appropriate proactive strategies in a dialog game setting for improving Human-AI teams.

翻译：人机协作的概念近年来受到越来越多的关注。为了实现人类与AI搭档之间的有效协作，主动性对于紧密协调和高效沟通至关重要。然而，如何为基于AI的系统设计恰当的主动性以支持人类，仍是一个开放性问题且颇具挑战性。本文提出了一种基于语料库的用户模拟器，用于训练和测试主动对话策略。该模拟器整合了有关主动对话及其对用户信任影响的先验知识，并模拟用户行为和个人信息（包括社会人口学特征与人格特质）。研究比较了两种不同的模拟方法，其中基于任务步骤的方法因能更好地建模序列依赖性而取得了更优的整体效果。本项研究为在对话游戏场景中探索和评估恰当的主动策略以改进人机协作提供了有前景的途径。