LSVF: a New Search Heuristic to Reduce the Backtracking Calls for Solving Constraint Satisfaction Problem

C. Rodrigues, R. Azevedo, F. Freitas, Eric Rommel Galvão Dantas
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Abstract

Many researchers in Artificial Intelligence seek for new algorithms to reduce the amount of memory/ time consumed for general searches in Constraint Satisfaction Problems. These improvements are accomplished by the use of heuristics which either prune useless tree search branches or even indicate the path to reach the (optimal) solution faster than the blind version of the search. Many heuristics were proposed in the literature, like the Least Constraining Value (LCV). In this paper we propose a new pre-processing search heuristic to reduce the amount of backtracking calls, namely the Least Suggested Value First: a solution whenever the LCV solely cannot measure how much a value is constrained. In this paper, we present a pedagogical example, as well as the preliminary results.
LSVF:求解约束满足问题的一种新的减少回溯调用的搜索启发式算法
许多人工智能研究人员寻求新的算法来减少约束满足问题中一般搜索所消耗的内存/时间。这些改进是通过使用启发式方法来完成的,启发式方法可以修剪无用的树搜索分支,甚至可以指示比盲目搜索更快到达(最佳)解决方案的路径。文献中提出了许多启发式方法,如最小约束值(LCV)。在本文中,我们提出了一种新的预处理搜索启发式方法来减少回溯调用的数量,即最小建议值优先:当LCV完全无法测量值的约束程度时的解决方案。在本文中,我们给出了一个教学实例,以及初步的结果。
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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