面向服务的多智能体网络的分布式搜索效率和鲁棒性

W. Ke
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引用次数: 0

摘要

本文研究了面向服务的大型智能体网络中的分散搜索,研究了多种因素对搜索效率的影响。在本研究中,我们关注整体系统的鲁棒性,并检查在不稳定环境中的搜索性能,其中单个代理可能失败或可能发生系统范围的攻击。实验结果表明,当大量服务代理不可用时,搜索仍然是有效的。令人惊讶的是,就搜索路径长度度量而言,整体系统性能随着不可用代理数量的增加而提高。服务不可用也会影响服务代理的负载平衡。我们计划进行进一步的研究,以验证观察到的模式,并了解系统架构设计的相关含义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Distributed Search Efficiency and Robustness in Service oriented Multi-agent Networks
We study decentralized searches in a large service-oriented agent network and investigate the influences of multiple factors on search efficiency. In this study we focus on overall system robustness and examine search performance in unstable environments where individual agents may fail or a system-wide attack may occur. Experimental results show that searches continue to be efficient when a large number of service agents become unavailable. Surprisingly, overall system performance in terms of a search path length metric improves with an increasing number of unavailable agents. Service unavailability also has an impact on the load balance of service agents. We plan to conduct further research to verify observed patterns and to understand related implications on system architecture design.
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