  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.
