Question

List the types of constraints in artificial intelligence

Answer 1

In artificial intelligence and operations research, constraint satisfaction is the process of finding a solution to a set of constraints that impose conditions that the variables must satisfy. A solution is therefore a set of values for the variables that satisfies all constraints—that is, a point in the feasible region.

The techniques used in constraint satisfaction depend on the kind of constraints being considered. Often used are constraints on a finite domain, to the point that constraint satisfaction problems are typically identified with problems based on constraints on a finite domain. Such problems are usually solved via search, in particular a form of backtracking or local search. Constraint propagation are other methods used on such problems; most of them are incomplete in general, that is, they may solve the problem or prove it unsatisfiable, but not always. Constraint propagation methods are also used in conjunction with search to make a given problem simpler to solve. Other considered kinds of constraints are on real or rational numbers; solving problems on these constraints is done via variable elimination or the simplex algorithm.

Answer 2

The types of constraints in artificial intelligence are:

• State constraints: These constraints include physical constraints on the given state or on the states and make sure to forbid the states that are against maintenance goals.

• Effect constraints: These constraints are among the state variables and action variables at given time t, state variables with time t + 1 along with previous state.

• Precondition constraints: These constraints are between the state variable of given time t and specify the actions from a state.

• Actions constraints: They state which actions cannot co-occur.

• Initial state constraints and goal constraints.

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