面向人机团队中统计主动对话建模的可信感知用户模拟器的开发
Development of a Trust-Aware User Simulator for Statistical Proactive Dialog Modeling in Human-AI Teams
AI总结：
本文开发了一个基于语料库的用户模拟器，用于训练和测试主动对话策略，以改善人机团队协作，并通过比较两种模拟方法发现基于任务步骤的方法效果更优。
摘要：
近年来，人机团队的概念日益受到关注。为了实现人类与AI队友之间的有效协作，主动性对于紧密协调和有效沟通至关重要。然而，为基于AI的系统设计足够的主动性以支持人类仍然是一个开放性问题且具有挑战性的课题。本文中，我们介绍了一个基于语料库的用户模拟器的开发，用于训练和测试主动对话策略。该模拟器融入了关于主动对话及其对用户信任影响的知情知识，并模拟用户行为和个人信息，包括社会人口特征和人格特质。我们比较了两种不同的模拟方法，其中基于任务步骤的方法由于增强了对序列依赖性的建模而取得了更好的整体结果。这项研究为在对话游戏环境中探索和评估适当的主动策略以改善人机团队提供了一条有前景的途径。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
