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A binary constraint is arc consistent if, for every value of one variable, there is a consistent value of the other variable. In CSP, enforcing arc consistency can reduce the search space.

The domain in CSP refers to the complete set of possible values that variables can assume.

A CSP involves finding a solution that satisfies a set of constraints over variables, where each variable has a specific domain of values it can take.

Backtracking is a brute-force search algorithm for solving CSPs that incrementally builds candidates to the solutions, and abandons a candidate ('backtracks') as soon as it determines that the candidate cannot possibly be completed to a valid solution.

In scheduling problems, CSP can be used to assign times and resources to tasks such that no two tasks overlap in time and resources are not over-allocated.

Constraint propagation is the process of determining how the constraints affect the possible values of a variable, often leading to domains being reduced.

Local search algorithms for CSPs start with an incomplete or incorrect solution and iteratively make changes that move towards a solution with fewer constraint violations.

Heuristics are strategies or methods applied to CSPs to decide the order of variable selection and value assignments in order to find a solution more efficiently.

Global constraints involve a large number of variables and often capture common patterns of constraints, such as 'all-different', which states that all variables must take on different values.