迟到的人有助于见面:飞机上的确定性匿名聚会

A. Pelc, R. Yadav
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引用次数: 1

摘要

一个由平面上自由移动的点代表的匿名移动代理团队必须聚集在一个点并停止。代理人从平面的不同点出发,并且可能在对手选择的不同时间出发。他们配备了指南针,一种常用的距离单位和时钟。它们执行相同的确定性算法。移动时,代理以归一化为1的相同速度行进。当智能体之间的距离最大为某个正的未知常数ε时,它们就能看到彼此,并且可以交换迄今为止已知的所有信息。由于代理的匿名性和面的对称性,收集是不可能的,例如,如果代理同时在大于λ的距离上开始。然而,如果一些代理相对于其他代理开始延迟,则可能会收集。在什么情况下,这些后来者可以使聚会成为可能?如何利用延迟使之成为可能?为了回答这些问题,我们考虑将初始构型形式化为对{(p1, t1), (p2, t2),…, (pn, tn)},对于n≥2,其中PI为第i个agent的起始点,ti为其起始时间。如果从初始配置开始的代理可以通过某种算法(甚至专门用于此特定配置)进行收集,那么初始配置就是可收集的。我们的第一个结果是所有可收集初始配置的特征。那么,人们自然会问,是否存在一种通用的确定性算法,可以收集给定大小的所有可收集配置。这个问题的答案是否定的。实际上,我们证明了所有可收集的配置都可以划分为两个集合:坏配置和好配置。我们展示了不良的可收集配置(即使大小为2)不能通过普通的收集算法收集。另一方面,我们证明了存在一种通用算法,它可以收集给定大小的所有好的配置。然后我们要问的问题是,要收集所有好的配置,是否需要知道代理的确切数量。结果证明答案是否定的,并且我们证明了关于收集所有良好配置的算法必须具有的代理数量的知识的充分必要条件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Latecomers Help to Meet: Deterministic Anonymous Gathering in the Plane
A team of anonymous mobile agents represented by points freely moving in the plane have to gather at a single point and stop. Agents start at different points of the plane and at possibly different times chosen by the adversary. They are equipped with compasses, a common unit of distance and clocks. They execute the same deterministic algorithm. When moving, agents travel at the same speed normalized to 1. When agents are at distance at most ϵ, for some positive constant ϵ unknown to them, they see each other and can exchange all information known to date. Due to the anonymity of the agents and the symmetry of the plane, gathering is impossible, e.g., if agents start simultaneously at distances larger than ϵ. However, if some agents start with a delay with respect to others, gathering may become possible. In which situations can such latecomers enable gathering? How can delays be used to make it possible? To answer these questions we consider initial configurations formalized as sets of pairs {(p1, t1), (p2, t2),..., (pn, tn)}, for n ≥ 2, where pi is the starting point of the i-th agent and ti is its starting time. An initial configuration is gatherable if agents starting at it can be gathered by some algorithm, even dedicated to this particular configuration. Our first result is a characterization of all gatherable initial configurations. It is then natural to ask if there is a universal deterministic algorithm that can gather all gatherable configurations of a given size. It turns out that the answer to this question is negative. Indeed, we show that all gatherable configurations can be partitioned into two sets: bad configurations and good configurations. We show that bad gatherable configurations (even of size 2) cannot be gathered by a common gathering algorithm. On the other hand, we prove that there is a universal algorithm that gathers all good configurations of a given size. Then we ask the question of whether the exact knowledge of the number of agents is necessary to gather all good configurations. It turns out that the answer is no, and we prove a necessary and sufficient condition on the knowledge concerning the number of agents that an algorithm gathering all good configurations must have.
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