Trade-offs between selection complexity and performance when searching the plane without communication

Trade-offs between selection complexity and performance when searching the plane without communication
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在没有通信的情况下搜索平面时选择复杂性和性能之间的权衡

DOI:
10.1145/2611462.2611463
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发表时间:
2014
期刊:
Proceedings of the 2014 ACM symposium on Principles of distributed computing
影响因子:
--
通讯作者:
Tsvetomira Radeva
Tsvetomira Radeva
中科院分区:
--
文献类型:
--
作者:
Christoph Lenzen;Nancy Lynch;Calvin Newport;Tsvetomira Radeva

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我们认为,在计算机科学中的生物启发问题的背景下,除了研究解决方案的时间复杂性,研究选择复杂性也很重要,这是一个衡量给定算法策略在自然界中出现的可能性的指标。本着这种精神,我们提出了一个选择复杂性度量x的蚂蚁问题[Feinerman等人]。对于算法A,我们定义χ(A)= B + log l,其中B是每个代理使用的存储器位数,l限制可用概率的精度(代理使用至少1/2l的概率)。我们考虑n个代理在平面上搜索目标,在距离原点的(未知)距离D内。我们确定log log D作为我们选择复杂性度量的关键阈值。我们证明了一个新的上界,当χ(A)≤ 3loglogD + O(1)时,该上界使(D2/n +D)<$2O(l)达到近优加速,当l∈ O(1)时,该上界是渐近最优的.相比之下,以前的算法实现类似的加速要求χ(A)= Ω(log D)。通过证明如果χ(A)< log log D - ω(1),则如果每个代理执行D2-o(1)移动,则目标很有可能找不到。这与直接的Ω(D2/n + D)下界构成了相当大的差距。
We argue that in the context of biology-inspired problems in computer science, in addition to studying thetime complexityof solutions it is also important to study theselection complexity, a measure of how likely a given algorithmic strategy is to arise in nature. In this spirit, we propose a selection complexity metric χ for the ANTS problem [Feinerman et al.]. For algorithm A, we define χ(A) = b + log l, where b is the number of memory bits used by each agent and l bounds the fineness of available probabilities (agents use probabilities of at least 1/2l). We consider n agents searching for a target in the plane, within an (unknown) distance D from the origin. We identify log log D as a crucial threshold for our selection complexity metric. We prove a new upper bound that achieves near-optimal speed-up of (D2/n +D) ⋅ 2O(l)for χ(A) ≤ 3 log log D + O(1), which is asymptotically optimal if l∈ O(1). By comparison, previous algorithms achieving similar speed-up require χ(A) = Ω(log D). We show that this threshold is tight by proving that if χ(A) < log log D - ω(1), then with high probability the target is not found if each agent performs D2-o(1)moves. This constitutes a sizable gap to the straightforward Ω(D2/n + D) lower bound.
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