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：近年来，人机协作概念日益受到关注。为实现人类与人工智能队友之间的有效协作，主动性对于密切协调和高效沟通至关重要。然而，如何为人工智能系统设计恰当的主动性以支持人类仍是一个开放且具有挑战性的课题。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。该模拟器融合了关于主动对话及其对用户信任影响的先验知识，并模拟用户行为与个人信息，包括社会人口学特征及个性特质。研究对比了两种不同的模拟方法，其中基于任务步骤的方法因增强了对时序依赖关系的建模而取得了更优的整体效果。本研究为在对话游戏情境中探索和评估提升人机协作效能的适当主动策略提供了有前景的途径。