
A new algorithm for constraint satisfaction problems with Maltsev templates
In this article, we provide a new algorithm for solving constraint satis...
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Piecewise Linear Valued Constraint Satisfaction Problems with Fixed Number of Variables
Many combinatorial optimisation problems can be modelled as valued const...
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An Algebraic Hardness Criterion for Surjective Constraint Satisfaction
The constraint satisfaction problem (CSP) on a relational structure B is...
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Solving Nurse Scheduling Problem Using Constraint Programming Technique
Staff scheduling is a universal problem that can be encountered in many ...
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ConstraintBased Visual Generation
In the last few years the systematic adoption of deep learning to visual...
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Protecting Privacy through Distributed Computation in Multiagent Decision Making
As largescale theft of data from corporate servers is becoming increasi...
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Automating Personnel Rostering by Learning Constraints Using Tensors
Many problems in operations research require that constraints be specifi...
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Possibilistic Constraint Satisfaction Problems or "How to handle soft constraints?"
Many AI synthesis problems such as planning or scheduling may be modelized as constraint satisfaction problems (CSP). A CSP is typically defined as the problem of finding any consistent labeling for a fixed set of variables satisfying all given constraints between these variables. However, for many real tasks such as jobshop scheduling, timetable scheduling, design?, all these constraints have not the same significance and have not to be necessarily satisfied. A first distinction can be made between hard constraints, which every solution should satisfy and soft constraints, whose satisfaction has not to be certain. In this paper, we formalize the notion of possibilistic constraint satisfaction problems that allows the modeling of uncertainly satisfied constraints. We use a possibility distribution over labelings to represent respective possibilities of each labeling. Necessityvalued constraints allow a simple expression of the respective certainty degrees of each constraint. The main advantage of our approach is its integration in the CSP technical framework. Most classical techniques, such as Backtracking (BT), arcconsistency enforcing (AC) or Forward Checking have been extended to handle possibilistics CSP and are effectively implemented. The utility of our approach is demonstrated on a simple design problem.
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