- Many problems in AI can be viewed as problems of constraint satisfaction in which the goal is to discover some problem state that satisfies a given set of constraints.
- Examples of this sort of problem include crypt-arithmetic puzzles.
- By viewing a problem as one of constraint satisfaction, it is often possible to reduce substantially the amount of search that is required as compared with a method that attempts to form partial solutions directly by choosing specific values.
- Constraint satisfaction is a search procedures that operates in a state space of constraint sets. a. The initial state contains the constraints that are originally given in the problem description. b. The goal state is a set which is to be achieved finally.
- Constraint satisfaction is a two step process 1. constraint are discovered and propagated as far as possible throughout the system. we also check constraint dependencies. 2. If the solution has not been achieved yet, second step begin
- After guessing , constraint propagation can begin again from this new state a. if a solution is found, it can be reported b. if still more guesses are required they can be made c. if a constraint is detected, then backtracking can be used to try a different guess and then proceed with it.
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