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Gathering Asynchronous Oblivious Agents with Local Vision in Regular Bipartite Graphs

机译:在规则二分图中收集具有本地视觉的异步遗忘代理

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We consider the problem of gathering identical, memoryless, mobile agents in one node of an anonymous graph. Agents start from different nodes of the graph. They operate in Look-Compute-Move cycles and have to end up in the same node. In one cycle, an agent takes a snapshot of its immediate neighborhood (Look), makes a decision to stay idle or to move to one of its adjacent nodes (Compute), and in the latter case makes an instantaneous move to this neighbor (Move). Cycles are performed asynchronously for each agent. The novelty of our model with respect to the existing literature on gathering asynchronous oblivious agents in graphs is that the agents have very restricted perception capabilities: they can only see their immediate neighborhood. An initial configuration of agents is called gatherable if there exists an algorithm that gathers all the agents of the configuration in one node and keeps them idle from then on, regardless of the actions of the asynchronous adversary. (The algorithm can be even tailored to gather this specific configuration.) The gathering problem is to determine which configurations are gatherable and find a (universal) algorithm which gathers all gatherable configurations. We give a complete solution of the gathering problem for regular bipartite graphs. Our main contribution is the proof that the class of gatherable initial configurations is very small: it consists only of "stars" (an agent A with all other agents adjacent to it) of size at least 3. On the positive side we give an algorithm accomplishing gathering for every gatherable configuration.
机译:我们考虑在匿名图的一个节点中收集相同的,无记忆的移动代理的问题。代理从图的不同节点开始。它们以Look-Compute-Move周期运行,并且必须终止于同一节点。在一个周期中,代理对它的近邻进行快照(查找),做出保持空闲或移动到其相邻节点之一的决定(计算),在后一种情况下,立即移动到该邻居(移动) )。周期是针对每个代理异步执行的。关于现有的关于在图表中收集异步遗忘的代理的文献,我们模型的新颖性在于代理具有非常有限的感知能力:它们只能看到它们的近邻。如果存在一种算法,该代理程序的初始配置称为可收集的,则该算法可将一个配置中的所有代理程序收集到一个节点中,并从那时起一直保持空闲状态,而与异步对手的行动无关。 (甚至可以对该算法进行定制以收集此特定配置。)收集问题是确定哪些配置可收集,并找到一个(通用)算法收集所有可收集的配置。我们给出正则二部图的聚集问题的完整解决方案。我们的主要贡献是证明可收集的初始配置的类别非常小:它仅由大小至少为3的“星形”(代理A和与其相邻的所有其他代理)组成。在积极方面,我们给出了一种算法完成每个可收集配置的收集。

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