Intelligent tutoring systems have proven very effective at teaching hard skills such as math and science, but less research has examined how to teach “soft” skills such as negotiation. In this paper, we introduce an effective approach to teaching negotiation tactics. Prior work showed that students can improve through practice with intelligent negotiation agents. We extend this work by proposing general methods of assessment and feedback that could be applied to a variety of such agents. We evaluate these techniques through a human subject study. Our study demonstrates that personalized feedback improves students’ use of several foundational tactics.
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Towards Emotion-Aware Agents For Negotiation Dialogues
Negotiation is a complex social interaction that encapsulates emotional encounters in human decision-making. Virtual agents that can negotiate with humans are useful in pedagogy and conversational AI. To advance the development of such agents, we explore the prediction of two important subjective goals in a negotiation – outcome satisfaction and partner perception. Specifically, we analyze the extent to which emotion attributes extracted from the negotiation help in the prediction, above and beyond the individual difference variables. We focus on a recent dataset in chat-based negotiations, grounded in a realistic camping scenario. We study three degrees of emotion dimensions – emoticons, lexical, and contextual by leveraging affective lexicons and a state-of-the-art deep learning architecture. Our insights will be helpful in designing adaptive negotiation agents that interact through realistic communication interfaces.
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- Award ID(s):
- 1852583
- PAR ID:
- 10313628
- Date Published:
- Journal Name:
- 9th International Conference on Affective Computing and Intelligent Interaction (ACII)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ACII52823.2021.9597427
