改进求解约束满足问题粗粒度弧相容算法

摘要

约束满足问题在人工智能领域有着广泛的应用.研究了约束满足问题的粗粒度维持弧相容求解算法,发现在求解过程中,对于指向已赋值变量的弧存在无效的修正检查,证明了这类修正检查是冗余的.提出一种方法避免这类冗余的修正检查,给出改进后的粗粒度弧相容算法的基本框架AC3_frame_ARR,该改进框架可用于改进所有粗粒度弧相容算法.实验结果表明,经过AC3 frame ARR改进后的算法最多可以节省80％的修正检查次数和40％的求解耗时.%Constraint satisfaction problems have been widely investigated in artificial intelligence area. This paper investigates whether the coarse-grained maintaining arc is consistent, which is used to solve CSPs, The study has found that during the search for a solution, there are ineffective revisions, which revise the arcs that point to assigned variables. This study has proved that such revisions are redundant and proposed a method to avoid such redundant revisions. The paper gives the improved basic frame for the coarse-grained arc consistency algorithms, named AC3_frame_ARR. The new frame can be applied to improve all the coarse-grained AC algorithms. The experimental results show that the improved algorithms can save at most 80% revisions and 40% CPU time.