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求解QBF 问题的启发式调查传播算法
引用本文:殷明浩,周俊萍,孙吉贵,谷文祥. 求解QBF 问题的启发式调查传播算法[J]. 软件学报, 2011, 22(7): 1538-1550. DOI: 10.3724/SP.J.1001.2011.03859
作者姓名:殷明浩  周俊萍  孙吉贵  谷文祥
作者单位:1. 东北师范大学计算机学院,吉林长春130117;吉林大学教育部符号计算与知识工程重点实验室,吉林长春130012
2. 东北师范大学计算机学院,吉林长春130117;吉林大学计算机科学与技术学院,吉林长春130012
3. 吉林大学教育部符号计算与知识工程重点实验室,吉林长春,130012
4. 东北师范大学计算机学院,吉林长春130117;
摘    要:提出了一种启发式调查传播算法,并基于该算法设计了一种QBF(quantified Boolean formulae)求解器——HSPQBF(heuristic survey propagation algorithm for solving QBF)系统.它将Survey Propagation信息传递方法应用到QBF求解问题中.利用Survey Propagation作为启发式引导DPLL(Davis,Putnam,Logemann and Loveland)算法,选择合适的变量进行分支,从而可以减小搜索空间,并减少算法回退的次数.在分支处理过程中,HSPQBF系统结合了单元传播、冲突学习和满足蕴涵学习等一些优秀的QBF求解技术,从而能够提高QBF问题的求解效率.实验结果表明,HSPQBF无论在随机问题上还是在QBF标准测试问题上都有很好的表现,验证了调查传播技术在QBF问题求解中的实际价值.

关 键 词:人工智能  QBF问题  QBF问题求解器  因子图  调查传播  冲突学习  满足蕴涵学习
收稿时间:2008-07-18
修稿时间:2010-03-29

Heuristic Survey Propagation Algorithm for Solving QBF Problem
YIN Ming-Hao,ZHOU Jun-Ping,SUN Ji-Gui and GU Wen-Xiang. Heuristic Survey Propagation Algorithm for Solving QBF Problem[J]. Journal of Software, 2011, 22(7): 1538-1550. DOI: 10.3724/SP.J.1001.2011.03859
Authors:YIN Ming-Hao  ZHOU Jun-Ping  SUN Ji-Gui  GU Wen-Xiang
Affiliation:1(College of Computer Science,Northeast Normal University,Changchun 130117,China) 2(College of Computer Science and Technology,Jilin University,Changchun 130012,China) 3(Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Changchun 130012,China)
Abstract:This paper presents a heuristic survey propagation algorithm for solving Quantified Boolean Formulae(QBF) problem. A QBF solver based on the algorithm is designed, namely HSPQBF (heuristic survey propagationalgorithm for solving QBF). This solver is a QBF reasoning engine that incorporates Survey Propagation method forproblem solving. Using the information obtained from the survey propagation procedure, HSPQBF can select abranch accurately. Furthermore, when handling the branches, HSPQBF uses efficient technology to solve QBFproblems, such as unit propagation, conflict driven learning, and satisfiability directed at implication and learning.The experimental results also show that HSPQBF can solve both random and QBF benchmark problems efficiently,which validates the effect of using survey propagation in a QBF solving process.
Keywords:artificial intelligence  quantified Boolean formulae problem  QBF(quantified Boolean formulae)solver  factor graph  survey propagation  conflict driven learning  satisfiability directed implicationand learning
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