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arXiv 2005.12623cs.DCcs.MA
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确定性高维网格探索的紧边界

Tight Bounds for Deterministic High-Dimensional Grid Exploration

Sebastian Brandt, Julian Portmann, Jara Uitto

摘要:

我们研究的问题,探索一个有向网格与有限自动机管理的自主智能体。在二维网格的情况下,需要多少代理来探索网格,或者等价地,在网格中找到隐藏的宝藏,在同步和半同步设置中都是完全理解的。对于更高的维度,Dobrev、Narayanan、Opatrny和Pankratov [ICALP'19]最近表明,令人惊讶的是,(少量)恒定数量的主体就足以找到宝藏,与维度的数量无关,从而反驳了Cohen、Emek、Louidor和Uitto的猜想[SODA'17]。Dobrev等人留下了一个悬而未决的问题,即他们对代理数量的限制是否可以改进。我们肯定地回答这个问题的确定性有限自动机:我们表明,3同步和4半同步代理足以探索任何常数$n$的$n$维网格。的界限是最佳的,值得注意的是,匹配的下限已经在2维的情况下。我们的技术也可以用于解决Dobrev等人提出的其他开放性问题:我们证明了4个同步和5个半同步代理足以进行多项式时间的探索,并且我们表明,在自然的假设下,3个同步和4个半同步代理足以探索任意维度的无方向网格(这又是紧的)。

英文摘要:

We study the problem of exploring an oriented grid with autonomous agents governed by finite automata. In the case of a 2-dimensional grid, the question how many agents are required to explore the grid, or equivalently, find a hidden treasure in the grid, is fully understood in both the synchronous and the semi-synchronous setting. For higher dimensions, Dobrev, Narayanan, Opatrny, and Pankratov [ICALP'19] showed very recently that, surprisingly, a (small) constant number of agents suffices to find the treasure, independent of the number of dimensions, thereby disproving a conjecture by Cohen, Emek, Louidor, and Uitto [SODA'17]. Dobrev et al. left as an open question whether their bounds on the number of agents can be improved. We answer this question in the affirmative for deterministic finite automata: we show that 3 synchronous and 4 semi-synchronous agents suffice to explore an $n$-dimensional grid for any constant $n$. The bounds are optimal and notably, the matching lower bounds already hold in the 2-dimensional case. Our techniques can also be used to make progress on other open questions asked by Dobrev et al.: we prove that 4 synchronous and 5 semi-synchronous agents suffice for polynomial-time exploration, and we show that, under a natural assumption, 3 synchronous and 4 semi-synchronous agents suffice to explore unoriented grids of arbitrary dimension (which, again, is tight).

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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