Knowledge discovery for adaptive negotiation agents in e-marketplaces
Decision Support Systems
Mining Trading Partners' Preferences for Efficient Multi-Issue Bargaining in E-Business
Journal of Management Information Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Evolving best-response strategies for market-driven agents using aggregative fitness GA
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An agent negotiation system based on adaptive genetic algorithm
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
MACSIMA: on the effects of adaptive negotiation behavior in agent-based supply networks
MATES'09 Proceedings of the 7th German conference on Multiagent system technologies
Applying hybrid case-based reasoning in agent-based negotiations for supply chain management
Expert Systems with Applications: An International Journal
Significant Cancer Prevention Factor Extraction: An Association Rule Discovery Approach
Journal of Medical Systems
Bargaining strategies designed by evolutionary algorithms
Applied Soft Computing
Text mining and probabilistic language modeling for online review spam detection
ACM Transactions on Management Information Systems (TMIS)
A Fuzzy Logic System for Bargaining in Information Markets
ACM Transactions on Intelligent Systems and Technology (TIST)
Buyer behavior adaptation based on a fuzzy logic controller and prediction techniques
Fuzzy Sets and Systems
Learning opponent's preferences for effective negotiation: an approach based on concept learning
Autonomous Agents and Multi-Agent Systems
Non-Monotonic Modeling for Personalized Services Retrieval and Selection
International Journal of Systems and Service-Oriented Engineering
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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.