A Supervised Topic Model Approach to Learning Effective Styles within Human-Agent Negotiation
Authors: 
Yuyu Xu, David Jeong, Pedro Sequeira, Jonathan Gratch, Javed Aslam, Stacy MarsellaAuthors Info & Claims
Yuyu Xu, David Jeong, Pedro Sequeira, Jonathan Gratch, Javed Aslam, Stacy MarsellaAuthors Info & ClaimsPages 2047 - 2049
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.
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- A Supervised Topic Model Approach to Learning Effective Styles within Human-Agent Negotiation
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Published In
May 2020
2289 pages
ISBN:9781450375184
- General Chairs:
- Amal El Fallah Seghrouchni,
- Gita Sukthankar,
- Program Chairs:
- Bo An,
- Neil Yorke-Smith Yorke-Smith
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International Foundation for Autonomous Agents and Multiagent Systems
Richland, SC
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Published: 13 May 2020
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AAMAS '19
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AAMAS '19: International Conference on Autonomous Agents and Multiagent Systems
May 9 - 13, 2020
Auckland, New Zealand
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