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Weighted CSP, a MAX-CSP in which each violation of a constraint is weighted according to a predefined preference. Thus satisfying constraint with more weight is preferred. Fuzzy CSP model constraints as fuzzy relations in which the satisfaction of a constraint is a continuous function of its variables' values, going from fully satisfied to ...
algorithm MIN-CONFLICTS is input: console.csp, A constraint satisfaction problem. max_steps, The number of steps allowed before giving up. current_state, An initial assignment of values for the variables in the csp. output: A solution set of values for the variable or failure.
A direct correspondence between the constraint satisfaction problem and the homomorphism problem can be established. For a given constraint satisfaction problem, one can build a pair of relational structures, the first encoding the variables and the signatures of constraints, the second encoding the domains and the relations of the constraints.
The constrained-optimization problem (COP) is a significant generalization of the classic constraint-satisfaction problem (CSP) model. [1] COP is a CSP that includes an objective function to be optimized. Many algorithms are used to handle the optimization part.
Given a set S of clauses, the Max constraint satisfaction problem (CSP) is to find the maximum number (in the weighted case: the maximal sum of weights) of satisfiable clauses in S. Similarly, the Min CSP problem is to minimize the number of unsatisfied clauses.
A constraint optimization problem (COP) is a constraint satisfaction problem associated to an objective function. An optimal solution to a minimization (maximization) COP is a solution that minimizes (maximizes) the value of the objective function. During the search of the solutions of a COP, a user can wish for:
AC-3 operates on constraints, variables, and the variables' domains (scopes). A variable can take any of several discrete values; the set of values for a particular variable is known as its domain. A constraint is a relation that limits or constrains the values a variable may have. The constraint may involve the values of other variables.
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.