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约束分解技术回顾与展望 被引量:1

Retrospect and Prospect of Decomposition Technology
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摘要 约束满足问题是一个强有力的知识表示框架,可以有效地解决许多问题。由于约束满足问题一般情况下是NP难度的问题,因此通过约束分解来降低计算的开销具有十分重要的意义。主要描述约束分解在约束满足问题中的地位、经典的分解技术和约束分解技术的发展历史,然后简要地分析这些分解技术。介绍了关于约束分解研究的最新状况,并描述、分析和总结其主要求解思想。最后根据存在的问题与不足提出了下一步的工作方向和研究思路。 Constraint satisfaction problems formalism offers a powerful frame of knowledge representation that can solve many problems.But constraint satisfaction problems are often NP-hard problems,so it's very important to using decomposition to reduce the costs of computation.This paper mainly described the importance of the decomposition in constraint satisfaction problems,several classic decomposition technologies and history of decomposition,and then analyzed these technologies.We introduced several new technologies of decomposition and analyzed them,then made a summary.We proposed our next research ideas and direction according to the problems in these technologies.
出处 《计算机科学》 CSCD 北大核心 2011年第10期29-33,38,共6页 Computer Science
基金 国家自然科学基金(60973089 60873148 60773097 61003101) 吉林省科技发展计划项目基金(20101501 20100185 20090108 20080107) 浙江省自然科学基金(Y1100191) 欧盟合作项目(155776-EM-1-2009-1-IT-ERAMUN-DUS-ECW-L12) 吉林大学符号计算与知识工程教育部重点实验室开放项目(93K-17-2009-K05)资助
关键词 约束满足问题 约束分解技术 知识表示框架 Constraint satisfaction problems Decomposition technology Knowledge representation frame
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参考文献42

  • 1Dechter R. Constraint networks. Encyclopedia of Artificial Intelligence(2nd edn)[M]. New York.. Wiley, 1992 : 276-285.
  • 2J'egou P, Terrioux C. A Time-Space Trade-off for Constraint Networks Decomposition [C]// Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2004). 2004:234- 239.
  • 3Miklos Z. On the Parallel Complexity of Structural CSP Decomposition Methods, Algorithms and Complexity[C]//Proceedings of the Third ACiD Workshop. 2007:107-118.
  • 4Bibel W. Constraint satisfaction from a deductive viewpoint[J]. Artificial Intelligence, 1988,35 : 401-413.
  • 5Gyssens M, Jeavons P G, Cohen D A. Decomposing constraint satisfaction problems using database techniques[J]. Artificial Intelligence, 1994,66 : 57-89.
  • 6Gyssens M, Paredaens J, A decomposition methodology for cyclic databases[M]. Advances in Database Theory, Vol. 2, New York~ Plenum Press, 1984 : 85-122.
  • 7Kolaitis P G, Vardi M Y. Conjunctive-query containment and constraint satisfaction[C]//Proc. Syrup. on Principles of Database Systems(PODS-98). Seattle,WA,1998:205-213.
  • 8Kolaitis P G, Vardi M Y. Conjunctive-query containment and constraint satisfaction [J]. J. Comput. System Sci., 2000, 61: 302-332.
  • 9Chandra A K, Merlin P M. Optimal implementation of conjunctive queries in relational data bases[C]//STOC' 77. ACM, 1977:77-90.
  • 10Yannakakis M. Algorithms for aeyclic database schemes[C]// VLDB'81. IEEE Computer Society, 1981 : 82-89.

同被引文献9

  • 1Kanoh H, Hasegawa K, Matsumoto M, et al. Solving constraint satisfaction problems by a genetic algorithm adopting viral infection[C]//Intelligence and Systems, IEEE International Joint Symposia, 1996 : 67-73.
  • 2Mu Shengjing,Su Hongye,Mao Weijie,et al. A new ge- netic algorithm to handle the constrained optimization problem[C]//Proceedings of the 41st IEEE Conference on Decision and Control, 2002 : 739-740.
  • 3Dechter R, Meiri I, Pearl J. Temporal constraint net- works[J]. Artificial intelligence, 1991,49 ( 1 ) :61-95.
  • 4Homaifar A, Qi C X, Lai S H. Constrained optimization via genetic algorithms[J]. Simulation, 1994,62 (4): 242- 253.
  • 5Poljak S, Rendl F. Solving the max-cut problem using eigenvalues [J]. Discrete Applied Mathematics, 1995 (62) :249-278.
  • 6梁昔明,秦浩宇,龙文.一种求解约束优化问题的遗传算法[J].计算机工程,2010,36(14):147-149. 被引量:25
  • 7徐周波,古天龙,常亮.加权约束满足问题的符号ADD求解算法[J].模式识别与人工智能,2011,24(1):14-21. 被引量:5
  • 8徐周波,古天龙,常亮,李凤英.约束满足问题求解的符号OBDD桶消元算法[J].计算机科学,2011,38(7):200-202. 被引量:4
  • 9林济铿,王旭东,李胜文,吴鹏,邵广惠,徐兴伟,马新.基于含连通图约束的背包问题的图分割方法[J].中国电机工程学报,2012,32(10):134-141. 被引量:17

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