Some New Tractable Classes of CSPs and Their Relations with Backtracking Algorithms

In this paper, we investigate the complexity of algorithms for solving CSPs which are classically implemented in real practical solvers, such as Forward Checking or Bactracking with Arc Consistency (RFL or MAC).. We introduce a new parameter for measuring their complexity and then we derive new complexity bounds. By relating the complexity of CSP algorithms to graph-theoretical parameters, our analysis allows us to define new tractable classes, which can be solved directly by the usual CSP algorithms in polynomial time, and without the need to recognize the classes in advance. So, our approach allows us to propose new tractable classes of CSPs that are naturally exploited by solvers, which indicates new ways to explain in some cases the practical efficiency of classical search algorithms.

[1]  Philippe Jégou,et al.  A New Evaluation of Forward Checking and Its Consequences on Efficiency of Tools for Decomposition of CSPs , 2008, 2008 20th IEEE International Conference on Tools with Artificial Intelligence.

[2]  Francesca Rossi,et al.  Principles and Practice of Constraint Programming – CP 2003 , 2003, Lecture Notes in Computer Science.

[3]  Eugene C. Freuder,et al.  Contradicting Conventional Wisdom in Constraint Satisfaction , 1994, ECAI.

[4]  Peter Jeavons,et al.  Constraint Tractability Theory And Its Application to the Product Development Process for a Constraint−Based Scheduler , 1999 .

[5]  Rina Dechter,et al.  Tree Clustering for Constraint Networks , 1989, Artif. Intell..

[6]  Bernard A. Nadel,et al.  Tree search and ARC consistency in constraint satisfaction algorithms , 1988 .

[7]  Lorna Stewart,et al.  Complexity Results on Graphs with Few Cliques , 2007, Discret. Math. Theor. Comput. Sci..

[8]  Daniël Paulusma,et al.  Satisfiability of acyclic and almost acyclic CNF formulas , 2011, Theor. Comput. Sci..

[9]  R. Möhring Algorithmic graph theory and perfect graphs , 1986 .

[10]  David R. Wood,et al.  On the maximum number of cliques in a graph embedded in a surface , 2009, Eur. J. Comb..

[11]  Eugene C. Freuder A Sufficient Condition for Backtrack-Free Search , 1982, JACM.

[12]  Antoine Rauzy Polynomial Restrictions of SAT: What Can Be Done with an Efficient Implementation of the Davis and Putnam's Procedure? , 1995, CP.

[13]  G. Verfaillie,et al.  Décomposition arborescente et cohérence locale souple dans les CSP pondérés , 2006 .

[14]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[15]  David R. Wood,et al.  On the Maximum Number of Cliques in a Graph , 2006, Graphs Comb..

[16]  Daniël Paulusma,et al.  Satisfiability of Acyclic and almost Acyclic CNF Formulas (II) , 2011, SAT.

[17]  Georg Gottlob,et al.  A Comparison of Structural CSP Decomposition Methods , 1999, IJCAI.

[18]  Manuel Bodirsky,et al.  Equivalence Constraint Satisfaction Problems , 2012, CSL.

[19]  Peter Jeavons,et al.  Perfect Constraints Are Tractable , 2008, CP.

[20]  David A. Cohen,et al.  A New Classs of Binary CSPs for which Arc-Constistency Is a Decision Procedure , 2003, CP.

[21]  J. Moon,et al.  On cliques in graphs , 1965 .

[22]  Toby Walsh,et al.  Handbook of Constraint Programming , 2006, Handbook of Constraint Programming.

[23]  Philippe Jégou,et al.  A Generalization of Chordal Graphs and the Maximum Clique Problem , 1997, Inf. Process. Lett..

[24]  Robert M. Haralick,et al.  Increasing Tree Search Efficiency for Constraint Satisfaction Problems , 1979, Artif. Intell..

[25]  Philippe Jégou Decomposition of Domains Based on the Micro-Structure of Finite Constraint-Satisfaction Problems , 1993, AAAI.

[26]  Martin C. Cooper,et al.  Generalizing constraint satisfaction on trees: Hybrid tractability and variable elimination , 2010, Artif. Intell..

[27]  Martin C. Cooper,et al.  On Guaranteeing Polynomially Bounded Search Tree Size , 2011, International Conference on Principles and Practice of Constraint Programming.

[28]  Fabrice Bouquet,et al.  Using OBDDs to Handle Dynamic Constraints , 1997, Inf. Process. Lett..