Backtracing in Artificial Intelligence is a technique that is used in an algorithmic way to solve problems in a recursive fashion. Here, a problem is solved by building a solution in increments and later removing the solutions that do not satisfy the constraints of the problem at hand. There are three main types of problems in backtracking. They are decision problems, optimization problems, and enumeration problems. In the case of decision problems, obtaining a feasible solution is the primary goal. In the car does the optimization problem, the best possible solution is hunted. And, in the enumeration problem, every single feasible solution to the problem is obtained. To understand if backtracking can be an effective solution, the constraints of the problem must be clear and well-defined. Only then the concepts of dynamic programming can be implemented in the form of algorithms to solve these problems effectively.
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