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Constraint programming. Constraint programming (CP) [1] is a paradigm for solving combinatorial problems that draws on a wide range of techniques from artificial intelligence, computer science, and operations research. In constraint programming, users declaratively state the constraints on the feasible solutions for a set of decision variables.
Example of a Business Process Model and Notation for a process with a normal flow. Business Process Model and Notation (BPMN) is a graphical representation for specifying business processes in a business process model.
Constraint satisfaction. In artificial intelligence and operations research, constraint satisfaction is the process of finding a solution through a set of constraints that impose conditions that the variables must satisfy. [1] A solution is therefore an assignment of values to the variables that satisfies all constraints—that is, a point in ...
A biomechanics simulator is a simulation platform for creating dynamic mechanical models built from combinations of rigid and deformable bodies, joints, constraints, and various force actuators. It is specialized for creating biomechanical models of human anatomical structures, with the intention to study their function and eventually assist in ...
Constraint (mathematics) In mathematics, a constraint is a condition of an optimization problem that the solution must satisfy. There are several types of constraints—primarily equality constraints, inequality constraints, and integer constraints. The set of candidate solutions that satisfy all constraints is called the feasible set.
A constraint in computer-aided design (CAD) software is a limitation or restriction imposed by a designer or an engineer upon geometric properties [1] : 203 of an entity of a design model that maintains its structure as the model is manipulated. These properties can include relative length, angle, orientation, size, shift, and displacement. [2]
Constraint learning. In constraint satisfaction backtracking algorithms, constraint learning is a technique for improving efficiency. It works by recording new constraints whenever an inconsistency is found. This new constraint may reduce the search space, as future partial evaluations may be found inconsistent without further search.
The algorithm works by creating the constraint satisfied by these evaluations and incorporating this new constraint in the second node. When all constraints have been propagated from the leaves to the root and back to the root, all nodes contain all constraints that are relevant to them. The problem can therefore be solved in each node.