Cyclic consistency: A local reduction operation for binary valued constraints (original) (raw)
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Arc-Consistency algorithms are the most commonly used filtering techniques to prune the search space in Constraint Satisfaction Problems (CSPs). 2-consistency is a similar technique that guarantees that any instantiation of a value to a variable can be consistently extended to any second variable. Thus, 2-consistency can be stronger than arc-consistency in binary CSPs. In this work we present a new algorithm to achieve 2consistency called 2-C4. This algorithm is a reformulation of AC4 algorithm that is able to reduce unnecessary checking and prune more search space than AC4. The experimental results show that 2-C4 was able to prune more search space than arc-consistency algorithms in non-normalized instances. Furthermore, 2-C4 was more efficient than other 2-consistency algorithms presented in the literature.
AC2001-OP: an arc-consistency algorithm for constraint satisfaction problems
Trends in Applied Intelligent Systems, 2010
Arc-consistency algorithms are widely used to prune the search space of Constraint Satisfaction Problems (CSPs). One of the most well-known arc-consistency algorithms for filtering CSPs is AC3. This algorithm repeatedly carries out revisions and requires support checks for identifying and deleting all unsupported values from the domains. Nevertheless, many revisions are ineffective, that is, they cannot delete any value and they require a lot of checks and are time-consuming. We present AC3-OP, an optimized and reformulated version of AC3 that reduces the number of constraint checks and prunes the same CSP search space with arithmetic constraints. In inequality constraints, AC3-OP, checks the binary constraints in both directions (full arc-consistency), but it only propagates new constraints in one direction. Thus, it avoids checking redundant constraints that do not filter any value of the variable's domain. The evaluation section shows the improvement of AC3-OP over AC3 in random instances.
Domain Reduction for Valued Constraints by Generalising Methods from CSP
Principles and Practice of Constraint Programming, 2018
For classical CSPs, the absence of broken triangles on a pair of values allows the merging of these values without changing the satisfiability of the instance, giving experimentally verified reduction in search time. We generalise the notion of broken triangles to VCSPs to obtain a tractable value-merging rule which preserves the cost of a solution. We then strengthen this value merging rule by using soft arc consistency to remove soft broken triangles and we show that the combined rule generalises known notions of domain value substitutability and interchangeability. Unfortunately the combined rules are no longer tractable to apply, but can still have applications as heuristics for reducing the search space. Finally we consider the generalisation of another value-elimination rule for CSPs to binary VCSPs. This new rule properly generalises neighbourhood substitutability and so we expect it will also have practical applications.
Beyond NP: Arc-Consistency for Quantified Constraints
Lecture Notes in Computer Science, 2002
The generalization of the satisfiability problem with arbitrary quantifiers is a challenging problem of both theoretical and practical relevance. Being PSPACE-complete, it provides a canonical model for solving other PSPACE tasks which naturally arise in AI. Effective SAT-based solvers have been designed very recently for the special case of boolean constraints. We propose to consider the more general problem where constraints are arbitrary relations over finite domains. Adopting the viewpoint of constraint-propagation techniques so successful for CSPs, we provide a theoretical study of this problem. Our main result is to propose quantified arc-consistency as a natural extension of the classical CSP notion.
A fine-grained arc-consistency algorithm for non-normalized constraint satisfaction problems
International Journal of Applied Mathematics and Computer Science, 2011
Constraint programming is a powerful software technology for solving many real-life problems. Many of these problems can be modeled as constraint satisfaction problems (CSPs) and can be solved using constraint programming techniques. However, solving a CSP is NP-Complete so that filtering techniques to reduce the search space are still necessary. Arcconsistency algorithms are widely used to prune the search space. The concept of arc-consistency is bidirectional, that is, it must be ensured in both directions of the constraint (direct constraint and inverse constraint). Two of the most wellknown and frequently used arc-consistency algorithms for filtering CSPs are AC3 and AC4. These algorithms repeatedly carry out revisions and they require support checks for identifying and deleting all unsupported values from the domains. Nevertheless, many revisions are ineffective, that is, they cannot delete any value and they consume a lot of checks and time.
Valued Constraint Satisfaction Problems: Hard and Easy Problems
1995
In order to deal with over-constrained Constraint Satisfaction Problems, various extensions of the CSP framework have been considered by taking into account costs, uncertainties, preferences, priorities...Each extension uses a specific mathematical operator (+, max...) to aggregate constraint violations. In this paper, we consider a simple algebraic framework, related to Partial Constraint Satisfaction, which subsumes most of these proposals and use it to characterize existing proposals in terms of rationality and computational complexity. We exhibit simple relationships between these proposals, try to extend some traditional CSP algorithms and prove that some of these extensions may be computationally expensive.
Artificial Intelligence, 2002
There are two well known transformations from non-binary constraints to binary constraints applicable to constraint satisfaction problems (CSPs) with finite domains: the dual transformation and the hidden (variable) transformation. We perform a detailed formal comparison of these two transformations. Our comparison focuses on two backtracking algorithms that maintain a local consistency property at each node in their search tree: the forward checking and maintaining arc consistency algorithms. We first compare local consistency techniques such as arc consistency in terms of their inferential power when they are applied to the original (non-binary) formulation and to each of its binary transformations. For example, we prove that enforcing arc consistency on the original formulation is equivalent to enforcing it on the hidden transformation. We then extend these results to the two backtracking algorithms. We are able to give either a theoretical bound on how much one formulation is better than another, or examples that show such a bound does not exist. For example, we prove that the performance of the forward checking algorithm applied to the hidden transformation of a problem is within a polynomial bound of the performance of the same algorithm applied to the dual transformation of the problem. Our results can be used to help decide if applying one of these transformations to all (or part) of a constraint satisfaction model would be beneficial.
AC3-OP: An Arc-Consistency Algorithm for Arithmetic Constraints
Proceeding of the 2009 conference on …, 2009
Arc-consistency algorithms are widely used to prune the search space of Constraint Satisfaction Problems (CSPs). One of the most well-known arc-consistency algorithms for filtering CSPs is AC3. This algorithm repeatedly carries out revisions and requires support checks for identifying and deleting all unsupported values from the domains. Nevertheless, many revisions are ineffective, that is, they cannot delete any value and they require a lot of checks and are time-consuming. We present AC3-OP, an optimized and reformulated version of AC3 that reduces the number of constraint checks and prunes the same CSP search space with arithmetic constraints. In inequality constraints, AC3-OP, checks the binary constraints in both directions (full arc-consistency), but it only propagates new constraints in one direction. Thus, it avoids checking redundant constraints that do not filter any value of the variable's domain. The evaluation section shows the improvement of AC3-OP over AC3 in random instances.