Premium
An evolutionary learning approach for adaptive negotiation agents
Author(s) -
Lau Raymond Y.K.,
Tang Maolin,
Wong On,
Milliner Stephen W.,
Chen YiPing Phoebe
Publication year - 2006
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20120
Subject(s) - negotiation , computer science , context (archaeology) , pareto optimal , knowledge management , domain (mathematical analysis) , artificial intelligence , management science , machine learning , multi objective optimization , mathematics , engineering , political science , law , paleontology , biology , mathematical analysis
Developing effective and efficient negotiation mechanisms for real‐world applications such as e‐business is challenging because negotiations in such a context are characterized 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 article 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 that guarantees Pareto optimal. Our research work opens the door to the development of practical negotiation systems for real‐world applications. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 41–72, 2006.