An evolutionary learning approach for adaptive negotiation agents

Lau, Raymond Y.K., Tang, Maolin, Wong, On, Milliner, Stephen W., & Chen, Yi-Ping Phoebe (2006) An evolutionary learning approach for adaptive negotiation agents. International Journal of Intelligent Systems, 21(1), pp. 41-72.

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Developing effective and efficient negotiation mechanisms for real-world applications such as e-Business is challenging since negotiations in such a context are characterised by combinatorially complex negotiation spaces, tough deadlines, very limited information about the opponents, and volatile negotiator preferences. Accordingly, practical negotiation systems should be empowered by effective learning mechanisms to acquire dynamic domain knowledge from the possibly changing negotiation contexts. This paper illustrates our adaptive negotiation agents which are underpinned by robust evolutionary learning mechanisms to deal with complex and dynamic negotiation contexts. Our experimental results show that GA-based adaptive negotiation agents outperform a theoretically optimal negotiation mechanism which guarantees Pareto optimal. Our research work opens the door to the development of practical negotiation systems for real-world applications.

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46 citations in Scopus
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31 citations in Web of Science®

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ID Code: 6014
Item Type: Journal Article
Refereed: Yes
Keywords: Adaptive Negotiation, Artificial Intelligence, Agents, Negotiation
DOI: 10.1002/int.20120
ISSN: 0884-8173
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Neural Evolutionary and Fuzzy Computation (080108)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > School of Information Systems
Copyright Owner: Copyright 2006 John Wiley & Sons
Copyright Statement: The definite version is available on publication at
Deposited On: 19 Jan 2007 00:00
Last Modified: 29 Feb 2012 13:20

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