基于MACR和CAL启发式的求差知识编译算法  

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

作　　者：牛当当 吕帅[2,5] 王金艳[6] 刘斌[1,3,4] NIU Dang-dang;LU Shuai;WANG Jin-yan;LIU Bin(College of Information Engineering,Northwest A&F University,Yangling,Shaanxi 712100,China;College of Computer Science and Technology,Jilin University,Changchun,Jilin 130012,China;Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service(Northwest A&F University),Yangling,Shaanxi 712100,China;Key Laboratory of Agricultural Internet of Things,Ministry of Agriculture and Rural Affairs(Northwest A&F University),Yangling,Shaanxi 712100,China;Key Laboratory of Symbolic Computation and Knowledge Engineering(Jilin University),Ministry of Education,Changchun,Jilin 130012,China;School of Computer Science and Information Engineering,Guangxi Normal University,Guilin,Guangxi 541004,China)

机构地区：[1]西北农林科技大学信息工程学院,陕西杨凌712100 [2]吉林大学计算机科学与技术学院,吉林长春130012 [3]陕西省农业信息感知与智能服务重点实验室(西北农林科技大学),陕西杨凌712100 [4]农业农村部农业物联网重点实验室(西北农林科技大学),陕西杨凌712100 [5]符号计算与知识工程教育部重点实验室(吉林大学),吉林长春130012 [6]广西师范大学计算机科学与信息工程学院,广西桂林541004

出　　处：《电子学报》2020年第2期285-290,共6页Acta Electronica Sinica

基　　金：国家自然科学基金(No.61602388,No.61763003);吉林省科技发展计划资助项目(No.20180101053JC);陕西省自然科学基础研究计划项目(No.2017JM6059);中央高校基本科研业务费专项资金(No.2452019064);中国博士后科学基金(No.2017M613216);陕西省博士后基金(No.2016BSHEDZZ121);陕西省重点研发计划项目(No.2019ZDLNY07-06-01);广西自然科学基金(No.2016GXNSFAA380192);西北农林科技大学博士科研启动基金(No.Z109021813);广西多源信息挖掘与安全重点实验室开放基金(No.MIMS19-05)。

摘　　要：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.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 a-mount,which is used to dynamically select the clause of maximum complementary amount with middle result.For comple-mentary 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 signifi-cantly improve the compilation efficiency of DKCHER on the instances with big ratio of clause number with variable num-ber.MACR_CAL heuristics can improve the efficiency of DKCHER by 12 times in the best case.But MACR_CAL heuris-tics reduces the compilation quality of DKCHER.

关 键 词：知识编译 扩展规则 超扩展规则 EPCCL理论 启发式策略 

分 类 号：TP301[自动化与计算机技术—计算机系统结构] TP181[自动化与计算机技术—计算机科学与技术]