arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2003.06485cs.DC
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人群方案的稳健比较

Robust Comparison in Population Protocols

Dan Alistarh, Martin Töpfer, Przemysław Uznański

摘要:

最近,人们对种群模型的计算和复杂性特性产生了浓厚的兴趣,该模型假设$n$是匿名的,计算有界的节点,随机交互,并试图联合计算全局谓词。在这个模型中,研究多数和共识动态的工作已经取得了重大进展:假设每个节点最初处于$X$或$Y$两个状态之一,确定哪个状态具有更高的初始计数。在本文中,我们考虑了多数/共识的自然推广,我们称之为比较。我们有两个基线状态,$X_0$和$Y_0$,在任何初始配置中以固定的、可能很小的计数出现。重要的是,其中一个状态的计数比另一个高:我们将假设$|X_0| \ge C |Y_0|$为某个常数$C$。挑战在于设计一种协议,该协议可以快速可靠地决定基线状态$X_0$和$Y_0$中的哪一个具有更高的初始计数。我们提出了一个简单的算法解决比较:基线算法使用每个节点的$O(\log n)$状态,并在$O(\log n)$(并行)时间内以高概率收敛到一个状态,在该状态下,全体人口以与初始$|X_0|$与$|Y_0|$浓度成比例的速率对$X$或$Y$的意见进行投票。然后,我们描述如何这样的输出,然后可以用来解决比较。该算法是自稳定的,在这个意义上,它收敛到正确的决定,即使相对计数的基线状态$X_0$和$Y_0$动态变化期间执行,和泄漏鲁棒的,在这个意义上,它可以承受虚假的错误反应。我们的分析依赖于一个新的鞅浓度的结果,涉及到人口协议的演变,其预期的(稳态)分析,这应该是广泛适用的背景下,人口协议和意见动态。

英文摘要:

There has recently been a surge of interest in the computational and complexity properties of the population model, which assumes $n$ anonymous, computationally-bounded nodes, interacting at random, and attempting to jointly compute global predicates. Significant work has gone towards investigating majority and consensus dynamics in this model: assuming that each node is initially in one of two states $X$ or $Y$, determine which state had higher initial count. In this paper, we consider a natural generalization of majority/consensus, which we call comparison. We are given two baseline states, $X_0$ and $Y_0$, present in any initial configuration in fixed, possibly small counts. Importantly, one of these states has higher count than the other: we will assume $|X_0| \ge C |Y_0|$ for some constant $C$. The challenge is to design a protocol which can quickly and reliably decide on which of the baseline states $X_0$ and $Y_0$ has higher initial count. We propose a simple algorithm solving comparison: the baseline algorithm uses $O(\log n)$ states per node, and converges in $O(\log n)$ (parallel) time, with high probability, to a state where whole population votes on opinions $X$ or $Y$ at rates proportional to initial $|X_0|$ vs. $|Y_0|$ concentrations. We then describe how such output can be then used to solve comparison. The algorithm is self-stabilizing, in the sense that it converges to the correct decision even if the relative counts of baseline states $X_0$ and $Y_0$ change dynamically during the execution, and leak-robust, in the sense that it can withstand spurious faulty reactions. Our analysis relies on a new martingale concentration result which relates the evolution of a population protocol to its expected (steady-state) analysis, which should be broadly applicable in the context of population protocols and opinion dynamics.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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