An Evolutionary Approach for Learning Opponent's Deadline and Reserve Points in Multi-Issue Negotiation

TitleAn Evolutionary Approach for Learning Opponent's Deadline and Reserve Points in Multi-Issue Negotiation
Publication TypeJournal Article
Year of Publication2018
AuthorsAyachi, R., H. Bouhani, and B. N. Amor
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
ISSN1989-1660
IssueRegular Issue
Volume5
Number3
Date Published12/2018
Pagination131-140
Abstract

The efficiency of automated multi-issue negotiation depends on the available information about the opponent. In a competitive negotiation environment, agents do not reveal their parameters to their opponents in order to avoid exploitation. Several researchers have argued that an agent's optimal strategy can be determined using the opponent's deadline and reserve points. In this paper, we propose a new learning agent, so-called Evolutionary Learning Agent (ELA), able to estimate its opponent's deadline and reserve points in bilateral multi-issue negotiation based on opponent's counter-offers (without any additional extra information). ELA reduces the learning problem to a system of non-linear equations and uses an evolutionary algorithm based on the elitism aspect to solve it. Experimental study shows that our learning agent outperforms others agents by improving its outcome in term of average and joint utility.

KeywordsAgents, Automated Negotiation, Deadline Learning, Differential Evolution Algorithm, Invasive Weed Optimization
DOI10.9781/ijimai.2018.08.001
URLhttp://www.ijimai.org/journal/sites/default/files/files/2018/08/ijimai_5_3_14_pdf_17519.pdf
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ijimai_5_3_14.pdf3.65 MB