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In constraint satisfaction, the AC-3 algorithm (short for Arc Consistency Algorithm #3) is one of a series of algorithms used for the solution of constraint satisfaction problems (or CSPs). It was developed by Alan Mackworth in 1977. The earlier AC algorithms are often considered too inefficient, and many of the later ones are difficult to ...
The most known and used forms of local consistency are arc consistency, hyper-arc consistency, and path consistency. The most popular constraint propagation method is the AC-3 algorithm, which enforces arc consistency. Local search methods are incomplete satisfiability algorithms. They may find a solution of a problem, but they may fail even if ...
Directional consistency is the variant of arc, path, and -consistency tailored for being used by an algorithm that assigns values to variables following a given order of variables. They are similar to their non-directional counterparts, but only require that a consistent assignment to some variables can be consistently extended to another ...
The most common way of using look-ahead for solving constraint satisfaction problems is the maintaining arc-consistency (MAC) algorithm. [2] Two other methods involving arc consistency are full and partial look ahead. They enforce arc consistency, but not for every pair of variables.
Dolby AC-3, Dolby Digital audio codec; AC-3 algorithm (Arc Consistency Algorithm 3), one of a series of algorithms used for the solution of constraint satisfaction problems (35414) 1998 AC3, a minor planet; AC-3, an IEC utilization category
Constraint propagation works by reducing domains of variables, strengthening constraints, or creating new ones. This leads to a reduction of the search space, making the problem easier to solve by some algorithms. Constraint propagation can also be used as an unsatisfiability checker, incomplete in general but complete in some particular cases.
An AC algorithm consists of two main components: an "actor" that determines which actions to take according to a policy function, and a "critic" that evaluates those actions according to a value function. [2] Some AC algorithms are on-policy, some are off-policy. Some apply to either continuous or discrete action spaces. Some work in both cases.
This hybrid algorithm is based on running search over a set of variables and inference over the other ones. In particular, backtracking or some other form of search is run over a number of variables; whenever a consistent partial assignment over these variables is found, inference is run over the remaining variables to check whether this partial assignment can be extended to form a solution.