A multilateral multi-issue negotiation protocol
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
A multi-issue negotiation protocol among agents with nonlinear utility functions
Multiagent and Grid Systems - Negotiation and Scheduling Mechanisms for Multiagent Systems
Multiagent and Grid Systems - Negotiation and Scheduling Mechanisms for Multiagent Systems
A preliminary result on a representative-based multi-round protocol for multi-issue negotiations
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Acquisition of a concession strategy in multi-issue negotiation
Web Intelligence and Agent Systems
Automated Sealed-Bid Negotiation Model for Multi-issue Based on Fuzzy Method
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Multi-issue negotiation protocol for agents: exploring nonlinear utility spaces
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Evolving best-response strategies for market-driven agents using aggregative fitness GA
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Classical negotiation models are based on a centralised decision making approach which assumes the availability of complete information about negotiators and unlimited computational resources. These negotiation mechanisms are ineffective for supporting real-world negotiations. This paper illustrates an agent-based distributive negotiation mechanism where each agent's decision making model is independent to each other and is underpinned by an effective evolutionary learning algorithm to deal with complex and dynamic negotiation environments. Initial experimental results show that the proposed genetic algorithm (GA) based adaptive negotiation mechanism outperforms a theoretically optimal negotiation mechanism in environments constrained by limited computational resources and tough deadlines. Our research work opens the door to the development of practical negotiation systems for real-world applications.