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Memoryless search algorithms in a network with faulty advice

机译:建议中有错误的网络中无内存搜索算法

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In this paper, we present a randomized algorithm for a mobile agent to search for an item stored at a node t of a network, without prior knowledge of its exact location. Each node of the network has a database that will answer queries of the form "how do I find t?" by responding with the first edge on a shortest path to t. It may happen that some nodes, called liars, give bad advice. We investigate a simple memoryless algorithm which follows the advice with some fixed probability q > 1/2 and otherwise chooses a random edge. If the degree of each node and number of liars k are bounded, we show that the expected number of edges traversed by the agent before finding t is bounded from above by O(d+r{sup}k), where d is the distance between the initial and target nodes and r = q/(1-q). We also show that this expected number of steps can be significantly improved for particular topologies such as the complete graph and the torus.
机译:在本文中,我们提出了一种随机算法,供移动代理在不知道其确切位置的情况下搜索存储在网络节点t上的项目。网络的每个节点都有一个数据库,该数据库将回答“如何找到t?”形式的查询。通过以最短路径到达t来响应第一边。可能会发生某些称为骗子的节点提供错误建议的情况。我们研究了一种简单的无记忆算法,该算法以一定的固定概率q> 1/2遵循建议,否则选择随机边缘。如果每个节点的度数和骗子数k是有界的,我们表明在找到t之前,智能体所遍历的边的预期数量从上方受O(d + r {sup} k)约束,其中d是距离初始节点和目标节点之间的距离为r = q /(1-q)。我们还表明,对于诸如完整图形和圆环之类的特定拓扑,此预期的步数可以得到显着改善。

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