Automated agents that proficiently negotiate with people: Can we keep people out of the evaluation loop

Raz Lin, Yinon Oshrat, Sarit Kraus

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

6 Scopus citations

Abstract

Research on automated negotiators has flourished in recent years. Among the important issues considered is how these automated negotiators can proficiently negotiate with people. To validate this, many experimentations with people are required. Nonetheless, conducting experiments with people is timely and costly, making the evaluation of these automated negotiators a very difficult process. Moreover, each revision of the agent's strategies requires to gather an additional set of people for the experiments. In this paper we investigate the use of Peer Designed Agents (PDAs) - computer agents developed by human subjects - as a method for evaluating automated negotiators. We have examined the negotiation results and its dynamics in extensive simulations with more than 300 human negotiators and more than 50 PDAs in two distinct negotiation environments. Results show that computer agents perform better than PDAs in the same negotiation contexts in which they perform better than people, and that on average, they exhibit the same measure of generosity towards their negotiation partners. Thus, we found that using the method of peer designed negotiators embodies the promise of relieving some of the need for people when evaluating automated negotiators.

Original languageEnglish
Title of host publicationNew Trends in Agent-Based Complex Automated Negotiations
EditorsTakayuki Ito, Minjie Zhang, Valentin Robu, Shaheen Fatima, Tokuro Matsuo
Pages57-80
Number of pages24
DOIs
StatePublished - 2012

Publication series

NameStudies in Computational Intelligence
Volume383
ISSN (Print)1860-949X

Bibliographical note

Funding Information:
This research is based upon work supported in part by the U.S. Army Research Laboratory and the U.S. Army Research Office under grant number W911NF-08-1-0144 and under NSF grant 0705587.

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