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Meta-level Coordination for Solving Distributed Negotiation Chains in Semi-cooperative Multi-agent Systems

Author

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  • Xiaoqin Shelley Zhang

    (University of Massachusetts at Dartmouth)

  • Victor Lesser

    (University of Massachusetts at Amherst)

Abstract

A negotiation chain is formed when multiple related negotiations are spread over multiple agents. In order to appropriately order and structure the negotiations occurring in the chain so as to optimize the expected utility, we present an extension to a single-agent concurrent negotiation framework. This work is aimed at semi-cooperative multi-agent systems, where each agent has its own goals and works to maximize its local utility; however, the performance of each individual agent is tightly related to other agents’ cooperation and the system’s overall performance. We introduce a pre-negotiation phase that allows agents to transfer meta-level information. Using this information, the agent can improve the accuracy of its local model about how other agents would react to the negotiations. This more accurate model helps the agent in choosing a better negotiation solution for a distributed negotiation chain problem. The agent can also use this information to allocate appropriate time for each negotiation, hence to find a good ordering of all related negotiations. The experimental data show that these mechanisms improve the agents’ and the system’s overall performance significantly.

Suggested Citation

  • Xiaoqin Shelley Zhang & Victor Lesser, 2013. "Meta-level Coordination for Solving Distributed Negotiation Chains in Semi-cooperative Multi-agent Systems," Group Decision and Negotiation, Springer, vol. 22(4), pages 681-713, July.
    • Handle: RePEc:spr:grdene:v:22:y:2013:i:4:d:10.1007_s10726-012-9287-5
    DOI: 10.1007/s10726-012-9287-5
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    References listed on IDEAS

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    1. Guoming Lai & Cuihong Li & Katia Sycara, 2006. "Efficient Multi-Attribute Negotiation with Incomplete Information," Group Decision and Negotiation, Springer, vol. 15(5), pages 511-528, September.
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