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In the general case, constraint problems can be much harder, and may not be expressible in some of these simpler systems. "Real life" examples include automated planning, [6] [7] lexical disambiguation, [8] [9] musicology, [10] product configuration [11] and resource allocation. [12] The existence of a solution to a CSP can be viewed as a ...
Other considered kinds of constraints are on real or rational numbers; solving problems on these constraints is done via variable elimination or the simplex algorithm. Constraint satisfaction as a general problem originated in the field of artificial intelligence in the 1970s (see for example (Laurière 1978)).
An example: a binary constraint satisfaction problem (join-tree clustering can also be applied to non-binary constraints.) This graph is not chordal (x3x4x5x6 form a cycle of four nodes having no chord). The graph is made chordal. The algorithm analyzes the nodes from x6 to x1.
Every constraint satisfaction problem and subset of its variables defines a relation, which is composed by all tuples of values of the variables that can be extended to the other variables to form a solution. Technically, this relation is obtained by projecting the relation having the solutions as rows over the considered variables.
In constraint satisfaction, local search is an incomplete method for finding a solution to a problem. It is based on iteratively improving an assignment of the variables until all constraints are satisfied. In particular, local search algorithms typically modify the value of a variable in an assignment at each step.
If the problem mandates that the constraints be satisfied, as in the above discussion, the constraints are sometimes referred to as hard constraints.However, in some problems, called flexible constraint satisfaction problems, it is preferred but not required that certain constraints be satisfied; such non-mandatory constraints are known as soft constraints.
In artificial intelligence and operations research, a Weighted Constraint Satisfaction Problem (WCSP), also known as Valued Constraint Satisfaction Problem (VCSP), is a generalization of a constraint satisfaction problem (CSP) where some of the constraints can be violated (according to a violation degree) and in which preferences among solutions can be expressed.
The general constraint satisfaction problem consists in finding a list of integers x = (x[1], x[2], …, x[n]), each in some range {1, 2, …, m}, that satisfies some arbitrary constraint (Boolean function) F. For this class of problems, the instance data P would be the integers m and n, and the predicate F.