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.
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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|Item Type:||Journal Article|
|Keywords:||Adaptive Negotiation, Artificial Intelligence, Agents, Negotiation|
|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 www3.interscience.wiley.com|
|Deposited On:||19 Jan 2007|
|Last Modified:||29 Feb 2012 23:20|
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