A Supervised Topic Model Approach to Learning Effective Styles within Human-Agent Negotiation

Xu, Y., Jeong, D., Sequeira, P., Gratch, J., Aslam, J. and Marsella, S. (2020) A Supervised Topic Model Approach to Learning Effective Styles within Human-Agent Negotiation. In: 19th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS '20), Auckland, New Zealand, 9-13 May 2020, pp. 2047-2049. ISBN 9781450375184

Abstract

We present a method that analyzes a person's negotiation behavior to automatically detect co-occurrence of tactics and combination of tactics (i.e., negotiation styles). We first identify action features consistent with use of the common negotiation tactics based on prior research in negotiation. Next, we apply regularized linear regression over a negotiation dataset to assess how effective particular tactics are in predicting the negotiation outcome. Finally, we use a supervised variant of a topic model to derive effective negotiation styles. Results from the clusters produced by the topic models provide insights regarding the effectiveness of negotiation styles that people utilize.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Marsella, Professor Stacy
• Authors: Xu, Y., Jeong, D., Sequeira, P., Gratch, J., Aslam, J., and Marsella, S.
• College/School: College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
• ISBN: 9781450375184
• Copyright Holders: Copyright © 2020 International Foundation for Autonomous Agents and Multiagent Systems
• First Published: First published in Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS '20), 2047–2049
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher