Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Towards Genetically Optimised Multi-Agent Multi-Issue Negotiations
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 1 - Volume 01
Approximate and online multi-issue negotiation
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Multi-issue negotiation protocol for agents: exploring nonlinear utility spaces
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Preliminary Result on Secure Protocols for Multiple Issue Negotiation Problems
PRIMA '08 Proceedings of the 11th Pacific Rim International Conference on Multi-Agents: Intelligent Agents and Multi-Agent Systems
Finding Nash bargaining solutions for multi-issue negotiations: a preliminary result
HuCom '08 Proceedings of the 1st International Working Conference on Human Factors and Computational Models in Negotiation
PRIMA '09 Proceedings of the 12th International Conference on Principles of Practice in Multi-Agent Systems
Secure and efficient protocols for multiple interdependent issues negotiation
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Knowledge integration and management in autonomous systems
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Multi-issue negotiation protocols represent a promising field since most negotiation problems in the real world involve multiple issues. Our work focuses on negotiation with interdependent issues, in which agent utility functions are nonlinear. Existing works have not yet focused on agents' private information. In addition, they were not scalable in the sense that they have shown a high failure rate for making agreements among 5 or more agents. In this paper, we focus on a novel multi-round representative-based protocol that utilizes the amount of agents' private information revealed. Experimental results demonstrate that our mechanism reduces the failure rate in making agreements, and it is scalable on the number of agents compared with existing approaches.