2 research outputs found
A KNOWLEDGE REPRESENTATION FOR CONSTRAINT SATISFACTION PROBLEMS
In this paper we present a general representation for constraint satisfaction problems (CSP) and a -
framework for reasoning about their solution that unlike most constraint-based relaxation algorithms.
stresses the need for a "natural" encoding of constraint knowledge and can facilitate making inferences for
propagation, backtracking, and explanation. The representation consists of two components: a
generate-and-test problem solver which contains information about the problem variables, and a
constraint-driven reasoner that manages a set of constraints, specified as arbitrarily complex Boolean
expressions and represented in the form of a constraint network. This constraint network: incorporates
control information (reflected in 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.Information Systems Working Papers Serie
A Knowledge Representation for Constraint-Satisfaction Problems
Abstract-In this paper we present a general representation for constraint satisfaction problems (CSP) and a framework for reasoning about their solution that unlike most constraint-based relaxation algorithms. stresses the need for a "natural " encoding of constraint knowledge and can facilitate making inferences for propagation, backtracking, and explanation. The representation consists oi two componenrs: a generate-and-test problem solver which cclntains information about the problem variables, and a constraint-driven reasoner that manages a set of constraints, specified as arbitrarily complex Boolean expressions and represented in the form of a constraint network. This constraint network: incorporates control information (reflected in the syntax of the constraints) that is used for constaint propagaticn: 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. 1