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    基于约束图分片求解DCOP的Agent组织结构

    An Agent Organization Structure for Solving DCOP Based on the Partitions of Constraint Graph

    • 摘要: MAS中许多分布式推理问题可以建模为分布式约束优化问题(DCOP),解决DCOP的分布式算法已经成为MAS中的重要基础.已有的Adopt等算法通过对等的Agent之间的平等协商完成求解,强调了异步通信、分布计算与对解质量的保证,在求解问题的组织结构方面仍有改进余地.可以采用一种基于分散与集中相结合的思路,基于对约束图分片的方法及核心结点、通信主干道等概念,构造新颖的Agent组织结构,完成DCOP 问题的异步、分布求解.在该组织结构下求解DCOP的算法可在效率、适应动态性方面得到改善,并将一个Agent一个变量和一个Agent多个变量的DCOP求解方法统一起来.

       

      Abstract: The distributed constraint optimization problem (DCOP) is able to model a wide variety of distributed reasoning problems that arise in multiagent systems (MAS), and distributed algorithms of solving DCOP have already become one of the most important bases of MAS. Some previous algorithms, which emphasize the asynchronous communication, distributed computation and quality guarantees, such as Adopt, can obtain optimal solution of DCOP by negotiation among peer to peer agents. However, there is still possibility to improve in organization structure of solving problems. A novel organization structure of multi-agent is put forward, which adopts the idea of combination of decentralization and centralization, the partitions of constraint graph method and the notions of core node, and the main communication road. The asynchronous and distributed algorithm of DCOP in this organization structure can improve the efficiency in execution and adaptation in dynamics. Moreover, it can unite the solving method of DCOP dealing with a variable per agent and multiple variables per agent.

       

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