9 research outputs found
Development of a Trust-Aware User Simulator for Statistical Proactive Dialog Modeling in Human-AI Teams
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.Comment: Preprint Version submitted to ACM UMA
Effects of Persuasive Dialogues: Testing Bot Identities and Inquiry Strategies
Intelligent conversational agents, or chatbots, can take on various
identities and are increasingly engaging in more human-centered conversations
with persuasive goals. However, little is known about how identities and
inquiry strategies influence the conversation's effectiveness. We conducted an
online study involving 790 participants to be persuaded by a chatbot for
charity donation. We designed a two by four factorial experiment (two chatbot
identities and four inquiry strategies) where participants were randomly
assigned to different conditions. Findings showed that the perceived identity
of the chatbot had significant effects on the persuasion outcome (i.e.,
donation) and interpersonal perceptions (i.e., competence, confidence, warmth,
and sincerity). Further, we identified interaction effects among perceived
identities and inquiry strategies. We discuss the findings for theoretical and
practical implications for developing ethical and effective persuasive
chatbots. Our published data, codes, and analyses serve as the first step
towards building competent ethical persuasive chatbots.Comment: 15 pages, 10 figures. Full paper to appear at ACM CHI 202