基于多智能体的资源搜索算法研究

M. Al-Asfoor, M. Fasli
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引用次数: 3

摘要

网络环境下的资源共享已经成为工业界和学术界不断发展的研究领域。在资源约束的动态环境中,资源发现在资源共享过程中起着至关重要的作用。本文提出了一种用于动态异构网络的分布式多智能体模型。此外,本文还研究了三种资源搜索算法:随机漫步、有向搜索和自组织有向搜索。提出了一种资源和任务描述之间的近似匹配机制,以解决准确率和速度之间的权衡问题。由于执行随机搜索可能导致遍历整个网络并增加失败率,我们引入了启发式定向搜索来克服这个问题。基于代理的亲密度,代理通过创建一个覆盖网络来重新组织自己,这些代理拥有语义上接近的资源,然后相应地指导搜索消息。所提出的解决方案已在仿真环境中实现,仿真结果表明在搜索时间(以跳数计算)方面有显著改善。
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A study of multi agent based resource search algorithms
Resource sharing in a networked environment has been an evolved field of research in both industry and academia. In a dynamic, resource constrained environment, resource discovery plays a vital role in the resource sharing process. A distributed multi agent model for a dynamic, heterogeneous network has been proposed in this paper. Furthermore, three resource search algorithms have been investigated, namely: Random Walk, Directed Search and Directed Search with Self-Organisation. An approximate matching mechanism between resources and tasks descriptions has been proposed as a solution for the trade-off problem between the accuracy and speed. As performing random search might lead to traverse the whole network and increase the failing ratio, we have introduced a heuristic directed search to overcome this problem. Based on the agents' closeness, the Agents re-organise themselves by creating an overlay network of agents which are holding semantically close resources and then direct the search message accordingly. The proposed solution has been implemented in a simulated environment and simulation results have shown a significant improvement in the search time (calculated as a number of hops).
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