A Study of Residual Supports in Arc Consistency
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 125-129.
Abstract
In an Arc Consistency (AC) algorithm, a residual support, or residue, is a support that has been stored during a previous execution of the procedure which determines if a value is supported by a constraint. The point is that a residue is not guaranteed to represent a lower bound of the smallest current support of a value. In this paper, we study the theoretical impact of exploiting residues with respect to the basic algorithm AC3. First, we prove that AC3rm (AC3 with multi-directional residues) is optimal for low and high constraint tightness. Second, we show that when AC has to be maintained during a backtracking search, MAC2001 presents, with respect to MAC3rm, an overhead in O(med) per branch of the binary tree built by MAC, where m denotes the number of refutations of the branch, e the number of constraints and d the greatest domain size of the constraint network. One consequence is that MAC3rm admits a better worst-case time complexity than MAC2001 for a branch involving m refutations when either m > d^2 or m > d and the tightness of any constraint is either low or high. Our experimental results clearly show that exploiting residues allows enhancing MAC and SAC algorithms.
@inproceedings{ijcai2007p18,
title = {A Study of Residual Supports in Arc Consistency},
author = {Lecoutre, Christophe and Hemery, Fred},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {125--129},
year = {2007},
url = {https://ijcai.org/proceedings/2007/18},
}
TY - CPAPER TI - A Study of Residual Supports in Arc Consistency AU - Lecoutre, Christophe AU - Hemery, Fred T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 125 EP - 129 UR - https://ijcai.org/proceedings/2007/18 ER -Copied
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