A Knowledge Representation for Constraint Satisfaction Problems

We present a general representation for problems that can be reduced to constraint satisfaction problems (CSP) and a model for reasoning about their solution. The novel part of the model is a constraint-driven reasoner that manages a set of constraints specified in terms of arbitrarily complex Boolean expressions and represented in the form of a dependency network. This dependency network incorporates control information (derived from the syntax of the constraints) that is used for constraint propagation, contains dependency information that can be used for explanation and for dependency-directed backtracking, and is incremental in the sense that if the problem specification is modified, a new solution can be derived by modifying the existing solution. The constraint-driven reasoner is coupled to a problem solver which contains information about the problem variables and preference orderings. >

[1]  Ellis L. Johnson,et al.  Solving Large-Scale Zero-One Linear Programming Problems , 1983, Oper. Res..

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

[3]  Geoffrey E. Hinton.  Relaxation and its role in vision , 1977 .

[4]  Bernard Nudel,et al.  Consistent-Labeling Problems and Their Algorithms: Expected-Complexities and Theory-Based Heuristics , 1983, Artif. Intell..

[5]  Vasek Chvátal,et al.  Edmonds polytopes and a hierarchy of combinatorial problems , 1973, Discret. Math..

[6]  Lawrence J. Watters.  Letter to the Editor - Reduction of Integer Polynomial Programming Problems to Zero-One Linear Programming Problems , 1967, Oper. Res..

[7]  Alan Bundy,et al.  Truth Maintenance System , 1984 .

[8]  Vasant Dhar,et al.  Experiments with an Integer Programming Formulation of an Expert System , 1989 .

[9]  James W. Goodwin,et al.  A Process Theory of Non-monotonic Inference , 1985, IJCAI.

[10]  Francesca Rossi,et al.  Constraint Relaxation may be Perfect , 1991, Artif. Intell..

[11]  Nesa L'abbe Wu,et al.  Linear programming and extensions , 1981 .

[12]  P. Hansen.  Methods of Nonlinear 0-1 Programming , 1979 .

[13]  Rina Dechter,et al.  Network-based heuristics for constraint satisfaction problems , 1988 .

[14]  Narendra Karmarkar,et al.  A new polynomial-time algorithm for linear programming , 1984, Comb..

[15]  Eugene C. Freuder.  Synthesizing constraint expressions , 1978, CACM.

[16]  Ugo Montanari,et al.  Networks of constraints: Fundamental properties and applications to picture processing , 1974, Inf. Sci..

[17]  Herbert A. Simon,et al.  The Structure of Ill Structured Problems , 1973, Artif. Intell..

[18]  D. McAllester,et al.  Dependency Directed Backtracking in Generalized Satisficing Assignment Problems , 1990 .

[19]  Jon Doyle,et al.  A Truth Maintenance System , 1979, Artif. Intell..

[20]  Rina Dechter,et al.  Network-Based Heuristics for Constraint-Satisfaction Problems , 1987, Artif. Intell..

[21]  David L. Waltz,et al.  Understanding Line drawings of Scenes with Shadows , 1975 .

[22]  Norman M. Sadeh,et al.  Constrained Heuristic Search , 1989, IJCAI.

[23]  Vasant Dhar,et al.  Rule-based versus structure-based models for explaining and generating expert behavior , 1987, CACM.

[24]  Alan K. Mackworth.  Consistency in Networks of Relations , 1977, Artif. Intell..

[25]  John N. Hooker,et al.  A quantitative approach to logical inference , 1988, Decis. Support Syst..

[26]  N. Biggs.  THE TRAVELING SALESMAN PROBLEM A Guided Tour of Combinatorial Optimization , 1986 .

[27]  Bernard Nudel,et al.  Solving the General Consistent Labeling (or Constraint Satisfaction) Problem: Two Algorithms and Their Expected Complexities , 1983, AAAI.

[28]  Christopher Alexander.  Notes on the Synthesis of Form , 1964 .

[29]  G. Lockhead.  Cognition and Thought , 1966 .