用一个不可靠的本地建议引导的代理来定位目标:当你有一个时钟时,如何击败随机漫步?

N. Hanusse, D. Ilcinkas, A. Kosowski, N. Nisse
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引用次数: 26

摘要

在Hanusse等人首先提出的模型[20,21]中,我们研究了移动智能体在具有非加权图结构的不可靠网络中寻找目标节点t的问题。网络的每个节点都能够给出关于下一个要访问的节点的建议,以便更接近目标t。不幸的是,被称为骗子的节点中有k个给出了不正确的建议。已知对于最大度为Δ≥3的n节点图G,到达距离初始位置d处的目标可能需要2Ω(min d,k})的期望时间,对于任意d,k = O(log n),即使G是树。本文重点研究了在每个节点上智能体只能选择遵循局部建议或随机选择一个事件边的情况下,有效解决搜索问题的策略。我们提出的策略称为R/A,它利用计时器(步长计数器)在忽略建议(R)和遵循建议(A)的阶段之间交替进行一定数量的步骤。不需要知道参数n、d或k,代理也不需要知道它从哪条边进入当前位置的节点。研究了两类具有展开极值的正则图的性能,即环图和随机Δ-regular图(一类重要的展开器)。对于环,R/A对于说谎者最坏情况分布的期望搜索时间为2d+kΘ(1),该分布在d和k上都是多项式。对于随机Δ-regular图,R/A策略的期望搜索时间为O(k3 log3 n) a.a.s.。关于n的多对数因子不能从该界中删除;事实上,我们表明,对于随机Ω-regular图A.A.S.中的所有d,k = Ω(log logn), Ω(log n)步长的较低时间范围都成立,甚至适用于利用一些环境知识的策略。最后,我们研究了不使用任何记忆(特别是没有计时器)的遗忘策略。这种策略本质上是一种随机漫步,可能会受到当地建议的影响。我们表明,这种有偏差的随机漫步有时比R/A策略的性能差得多。特别地,在环上,无偏随机漫步的搜索时间可以是d和k的多项式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Locating a target with an agent guided by unreliable local advice: how to beat the random walk when you have a clock?
We study the problem of finding a destination node t by a mobile agent in an unreliable network having the structure of an unweighted graph, in a model first proposed by Hanusse et al [20, 21]. Each node of the network is able to give advice concerning the next node to visit so as to go closer to the target t. Unfortunately, exactly k of the nodes, called liars, give advice which is incorrect. It is known that for an n-node graph G of maximum degree Δ ≥ 3, reaching a target at a distance of d from the initial location may require an expected time of 2Ω(min d,k}), for any d,k = O(log n), even when G is a tree. This paper focuses on strategies which efficiently solve the search problem in scenarios in which, at each node, the agent may only choose between following the local advice, or randomly selecting an incident edge. The strategy which we put forward, called R/A, makes use of a timer (step counter) to alternate between phases of ignoring advice (R) and following advice (A) for a certain number of steps. No knowledge of parameters n, d, or k is required, and the agent need not know by which edge it entered the node of its current location. The performance of this strategy is studied for two classes of regular graphs with extremal values of expansion, namely, for rings and for random Δ-regular graphs (an important class of expanders). For the ring, R/A is shown to achieve an expected searching time of 2d+kΘ(1) for a worst-case distribution of liars, which is polynomial in both d and k. For random Δ-regular graphs, the expected searching time of the R/A strategy is O(k3 log3 n) a.a.s. The polylogarithmic factor with respect to n cannot be dropped from this bound; in fact, we show that a lower time bound of Ω(log n) steps holds for all d,k = Ω(log logn) in random Ω-regular graphs a.a.s. and applies even to strategies which make use of some knowledge of the environment. Finally, we study oblivious strategies which do not use any memory (in particular, with no timer). Such strategies are essentially a form of a random walk, possibly biased by local advice. We show that such biased random walks sometimes achieve drastically worse performance than the R/A strategy. In particular, on the ring, no biased random walk can have a searching time which is polynomial in d and k.
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