The power of knowledge in linear search for an escaping target

IF 1.4 3区 计算机科学 Q1 BUSINESS, FINANCE
Journal of Computer and System Sciences Pub Date : 2026-05-01 Epub Date: 2025-12-08 DOI:10.1016/j.jcss.2025.103737
Jared Coleman , Dmitry Ivanov , Evangelos Kranakis , Danny Krizanc , Oscar Morales-Ponce
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引用次数: 0

Abstract

We consider linear search for an escaping target whose speed and/or initial distance from the origin may be unknown to the searcher. The searcher (an autonomous mobile agent) is initially placed at the origin of the real line and can move with maximum speed 1 in either direction along the line. The oblivious mobile target that is moving away from the origin with a constant speed v<1 is initially placed by an adversary on the infinite line at distance d from the origin in an unknown direction. We consider four cases, depending on whether v and/or d is known to the searcher. The main contributions of this paper are new lower bounds as well as algorithms leading to new upper bounds for search in these settings. We present tight bounds for the cases when v is known. For the cases where v is unknown, we prove an optimal (up to lower order terms in the exponent) competitive ratio in the case where d is known and improved upper and lower bounds for the case where d is unknown. These results solve an open problem proposed in Coleman et al. (2022) [11].
知识在线性搜索逃跑目标中的力量
我们考虑对逃逸目标的线性搜索,该目标的速度和/或到原点的初始距离对于搜索者来说可能是未知的。搜索器(一个自主移动代理)最初被放置在真实线路的原点,可以沿着线路以最大速度向任何方向移动。以恒定速度v<;1远离原点的遗忘移动目标最初被对手放置在距离原点d处的无限大线上,方向未知。我们考虑四种情况,取决于搜索者是否知道v和/或d。本文的主要贡献是新的下界以及在这些设置中导致新的上界搜索的算法。对于已知v的情况,我们给出了严格的边界。对于v未知的情况,我们证明了d已知情况下的最优竞争比(指数中的低阶项),并改进了d未知情况下的上界和下界。这些结果解决了Coleman等人(2022)提出的一个开放性问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Computer and System Sciences
Journal of Computer and System Sciences 工程技术-计算机:理论方法
CiteScore
3.70
自引率
0.00%
发文量
58
审稿时长
68 days
期刊介绍: The Journal of Computer and System Sciences publishes original research papers in computer science and related subjects in system science, with attention to the relevant mathematical theory. Applications-oriented papers may also be accepted and they are expected to contain deep analytic evaluation of the proposed solutions. Research areas include traditional subjects such as: • Theory of algorithms and computability • Formal languages • Automata theory Contemporary subjects such as: • Complexity theory • Algorithmic Complexity • Parallel & distributed computing • Computer networks • Neural networks • Computational learning theory • Database theory & practice • Computer modeling of complex systems • Security and Privacy.
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