Containment and evasion in stochastic point data
Abstract
Given two disjoint and finite point sets A and B in IRd, we say that B is contained in A if all the points of B lie within the convex hull of A, and that B evades A if no point of B lies inside the convex hull of A. We investigate the containment and evasion problems of this type when the set A is stochastic, meaning each of its points ai is present with an independent probability π(ai). Our model is motivated by situations in which there is uncertainty about the set A, for instance, due to randomized strategy of an adversarial agent or scheduling of monitoring sensors. Our main results include the following: (1) we can compute the exact probability of containment or evasion in two dimensions in worst-case O(n4 + m2) time and O(n2 + m2) space, where n = |A| and m = |B|, and (2) we prove that these problems are #P-hard in 3 or higher dimensions.
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Recommended Citation
Kumar, N., & Suri, S. (2016). Containment and evasion in stochastic point data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9644, 576-589. https://doi.org/10.1007/978-3-662-49529-2_43
