基于MACR和CAL启发式的求差知识编译算法

DKCHER算法是基于超扩展规则的求差知识编译算法.本文首先研究了DKCHER算法的执行流程，并定义了互补量的概念，然后设计了启发式策略MACR（maximum complementary amount of clauses with middle result），用于动态选择与中间结果互补量最大的子句.针对互补展开过程，设计了动态启发式策略CAL（optimal sequence sorted by complementary amount of literals），将互补展开中的文字按照与输入公式互补量的大小进行排序并展开.将上述两种启发式策略与DKCHER算法相结合，分别设计了MACR_DKCHER算法、CAL_DKCHER算法和MACR_CAL_DKCHER算法.实验结果表明，MACR启发式策略能够提升DKCHER算法的编译效率和编译质量，编译效率最高可提升9倍，编译质量最高可提升1.9倍；CAL启发式策略在子句数和变量数比值较大的实例上，能够提高DKCHER算法的编译效率，但会降低DKCHER算法的编译质量；MACR_CAL启发式最高可将DKCHER算法的编译效率提高12倍，但会导致DKCHER算法的编译质量有所降低.

Knowledge Compilation Algorithm of Computing Difference Based on MACR Heuristics and CAL Heuristics

DKCHER is a knowledge compilation algorithm of computing difference based on hyper extension rule.We study on the executing process of DKCHER algorithm in this paper, and define the concept of complementary amount.We design MACR (maximum complementary amount of clauses with middle result) heuristics based on complementary amount, which is used to dynamically select the clause of maximum complementary amount with middle result.For complementary unfolding in DKCHER, we design dynamic heuristics CAL (optimal sequence sorted by complementary amount of literals), which sort the literals in complementary unfolding based on their complementary amounts.Combining the above two heuristic methods with DKCHER, MACR_DKCHER algorithm, CAL_DKCHER algorithm and MACR_CAL_DKCHER algorithm are designed.Experimentally, MACR heuristics improves the compilation efficiency and compilation quality of DKCHER.MACR heuristics can improve the efficiency of DKCHER by 9 times in the best case.CAL heuristics can significantly improve the compilation efficiency of DKCHER on the instances with big ratio of clause number with variable number.MACR_CAL heuristics can improve the efficiency of DKCHER by 12 times in the best case.But MACR_CAL heuristics reduces the compilation quality of DKCHER.