In the previous chapter, we looked at solving search problems, which focused on the methodic evaluation of states and transitions between states. Every state transition typically involves a cost or gain, and the objective of the search was to minimize the cost or maximize the gain. Constraint satisfaction problems are a variant of search problems, where the states must satisfy a number of constraints or limitations. If we are able to translate the various violations of constraints into cost and then strive to minimize the cost, solving a constraint satisfaction problem can resemble solving a general search problem.
Like combinatorial optimization problems, constraint satisfaction problems have important applications in fields such as artificial intelligence, operations research, and pattern matching. A better understanding of these problems...
