发表机构
Carleton University（卡尔顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对m射线星形图，在两种通信模型和三种知识假设下，设计双机器人搜索策略以最小化捕获远离移动目标的竞争比，明确射线数量与通信模型对竞争比的影响。
AI 中文摘要
我们研究在m射线星形图中搜索移动目标的问题，这是线性搜索向多方向的自然推广。目标被对抗性地放置在某条射线上，可匀速移动。我们研究双机器人场景，其中协作与通信起核心作用，涉及两种通信模型：仅在相遇时通信的面对面（F2F）模型，以及非对称通信的发送者-接收者（S/R）模型。我们聚焦于远离模型，其中目标以v<1的速度远离原点移动。我们设计使竞争比最小化的搜索策略，并在三种知识假设下分析该问题：无距离（NoDistance）、无速度（NoSpeed）和无知识（NoKnowledge）。针对每种场景，我们推导竞争比的上界，部分场景还推导了下界。我们的结果凸显了射线数量与通信模型如何影响竞争比。
英文摘要
We study a problem of searching for a mobile target in an $m$-ray star graph, a natural generalization of linear search to multiple directions. The target is placed adversarially on one of the rays and may move with constant speed. We investigate a two-robot setting, where cooperation and communication play a central role. We study two communication models: the Face-to-Face (F2F) model, where robots communicate only upon meeting, and the Sender-Receiver (S/R) model, where communication is asymmetric. We focus on the {\em away model}, in which the target moves {\em away} from the origin with speed $v<1$. We design search strategies that minimize the competitive ratio and analyze the problem under three knowledge assumptions: \emph{NoDistance}, \emph{NoSpeed}, and \emph{NoKnowledge}. For each setting, we derive upper bounds on the competitive ratio and for some cases, we derive the lower bound. Our results highlight how the number of rays and the communication model influence the competitive ratio.
Comments12 pages
