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

“A Supervised Topic Model Approach to Learning Effective Styles within Human-Agent Negotiation” by Yuyu Xu, David Jeong, Pedro Sequeira, Jonathan Gratch, Javed Aslam, and Stacy Marsella. In Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, (Richland, SC), 2020.

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.

Keywords: agents competing and collaborating with humans, explainability in human-agent systems, socially interactive agents

BibTeX entry:

@inproceedings{xu2020supervised,
   author = {Yuyu Xu and David Jeong and Pedro Sequeira and Jonathan
	Gratch and Javed Aslam and Stacy Marsella},
   title = {A Supervised Topic Model Approach to Learning Effective Styles
	within Human-Agent Negotiation},
   booktitle = {Proceedings of the 19th International Conference on
	Autonomous Agents and MultiAgent Systems},
   series = {AAMAS '20},
   pages = {2047{\^a}2049},
   publisher = {International Foundation for Autonomous Agents and
	Multiagent Systems},
   address = {Richland, SC},
   year = {2020},
   isbn = {9781450375184},
   url = {https://dl.acm.org/doi/pdf/10.5555/3398761.3399070}
}

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