Results 1  10
of
48
Algorithms for Constraint Satisfaction Problems: A Survey
 AI MAGAZINE
, 1992
"... A large variety of problems in Artificial Intelligence and other areas of computer science can be viewed as a special case of the constraint satisfaction problem. Some examples are machine vision, belief maintenance, scheduling, temporal reasoning, graph problems, floor plan design, planning genetic ..."
Abstract

Cited by 372 (0 self)
 Add to MetaCart
A large variety of problems in Artificial Intelligence and other areas of computer science can be viewed as a special case of the constraint satisfaction problem. Some examples are machine vision, belief maintenance, scheduling, temporal reasoning, graph problems, floor plan design, planning genetic experiments, and the satisfiability problem. A number of different approaches have been developed for solving these problems. Some of them use constraint propagation to simplify the original problem. Others use backtracking to directly search for possible solutions. Some are a combination of these two techniques. This paper presents a brief overview of many of these approaches in a tutorial fashion.
Contradicting Conventional Wisdom in Constraint Satisfaction
, 1994
"... . Constraint satisfaction problems have wide application in artificial intelligence. They involve finding values for problem variables where the values must be consistent in that they satisfy restrictions on which combinations of values are allowed. Two standard techniques used in solving such p ..."
Abstract

Cited by 206 (12 self)
 Add to MetaCart
. Constraint satisfaction problems have wide application in artificial intelligence. They involve finding values for problem variables where the values must be consistent in that they satisfy restrictions on which combinations of values are allowed. Two standard techniques used in solving such problems are backtrack search and consistency inference. Conventional wisdom in the constraint satisfaction community suggests: 1) using consistency inference as preprocessing before search to prune values from consideration reduces subsequent search effort and 2) using consistency inference during search to prune values from consideration is best done at the limited level embodied in the forward checking algorithm. We present evidence contradicting both pieces of conventional wisdom, and suggesting renewed consideration of an approach which fully maintains arc consistency during backtrack search. 1 Introduction Constraint satisfaction problems (CSPs) involve finding values for prob...
Scalebased description and recognition of planar curves and twodimensional shapes
, 1986
"... The problem of finding a description, at varying levels of detail, for planar curves and matching two such descriptions is posed and solved in this paper. A number of necessary criteria are imposed on any candidate solution method. Pathbased Gaussian smoothing techniques are applied to the curve to ..."
Abstract

Cited by 166 (1 self)
 Add to MetaCart
The problem of finding a description, at varying levels of detail, for planar curves and matching two such descriptions is posed and solved in this paper. A number of necessary criteria are imposed on any candidate solution method. Pathbased Gaussian smoothing techniques are applied to the curve to find zeros of curvature at varying levels of detail. The result is the "generalized scale space " image of a planar curve which is invariant under rotation, uniform scaling and translation of the curve. These properties make the scale space image suitable for matching. The matching algorithm is a modification of the uniform cost algorithm and finds the lowest cost match of contours in the scale space images. It is argued that this is preferable to matching in a socalled stable scale of the curve because no such scale may exist for a given curve. This technique is applied to register a Landsat satellite image of the Strait of Georgia, B.C. (manually corrected for skew) to a map containing the shorelines of an overlapping area.
Arc Consistency for General Constraint Networks: Preliminary Results
, 1997
"... Constraint networks are used more and more to solve combinatorial problems in reallife applications. Much activity is concentrated on improving the efficiency of finding a solution in a constraint network (the constraint satisfaction problem, CSP). Particularly, arc consistency caught many research ..."
Abstract

Cited by 127 (15 self)
 Add to MetaCart
Constraint networks are used more and more to solve combinatorial problems in reallife applications. Much activity is concentrated on improving the efficiency of finding a solution in a constraint network (the constraint satisfaction problem, CSP). Particularly, arc consistency caught many researchers' attention, involving the discovery of a large number of algorithms. And, for the last two years, it has been shown that maintaining arc consistency during search is a worthwhile approach. However, results on CSPs and on arc consistency are almost always limited to binary constraint networks. The CSP is no longer an academic problem, and it is time to deal with nonbinary CSPs, as widely required in real world constraint solvers. This paper proposes a general schema to implement arc consistency on constraints of any arity when no specific algorithm is known. A first instantiation of the schema is presented here, which deals with constraints given by a predicate, by the set of forbidden c...
On the conversion between nonbinary and binary constraint satisfaction problems
, 1998
"... It is well known that any nonbinary discrete constraint satisfaction problem (CSP) can be translated into an equivalent binary CSP. Two translations are known: the dual graph translation and the hidden variable translation. However, there has been little theoretical or experimental work on how well ..."
Abstract

Cited by 88 (6 self)
 Add to MetaCart
It is well known that any nonbinary discrete constraint satisfaction problem (CSP) can be translated into an equivalent binary CSP. Two translations are known: the dual graph translation and the hidden variable translation. However, there has been little theoretical or experimental work on how well backtracking algorithms perform on these binary representations in comparison to their performance on the corresponding nonbinary CSP. We present both theoretical and empirical results to help understand the tradeoffs involved. In particular, we show that translating a nonbinary CSP into a binary representation can be a viable solution technique in certain circumstances. The ultimate aim of this research is to give guidance for when one should consider translating between nonbinary and binary representations. Our results supply some initial answers to this question.
An Optimal Coarsegrained Arc Consistency Algorithm
, 2001
"... The use of constraint propagation is the main feature of any constraint solver. It is thus of ..."
Abstract

Cited by 72 (11 self)
 Add to MetaCart
The use of constraint propagation is the main feature of any constraint solver. It is thus of
On Forward Checking for Nonbinary Constraint Satisfaction
 ARTIFICIAL INTELLIGENCE
, 1999
"... Solving nonbinary constraint satisfaction problems, a crucial challenge for the next years, can be tackled in two different ways: translating the nonbinary problem into an equivalent binary one, or extending binary search algorithms to solve directly the original problem. The latter option rai ..."
Abstract

Cited by 66 (4 self)
 Add to MetaCart
Solving nonbinary constraint satisfaction problems, a crucial challenge for the next years, can be tackled in two different ways: translating the nonbinary problem into an equivalent binary one, or extending binary search algorithms to solve directly the original problem. The latter option raises some issues when we want to extend denitions written for the binary case. This paper focuses on the wellknown forward checking algorithm, and shows that it can be generalized to several nonbinary versions, all tting its binary denition. The classical version, proposed by Van Hentenryck, is only one of these generalizations.
Local and global relational consistency
 THEORETICAL COMPUTER SCIENCE
, 1997
"... Local consistency has proven to be an important concept in the theory and practice of constraint networks. In this paper, we present a new definition of local consistency, called relational consistency. The new definition is relationbased, in contrast with the previous definition of local consiste ..."
Abstract

Cited by 61 (15 self)
 Add to MetaCart
Local consistency has proven to be an important concept in the theory and practice of constraint networks. In this paper, we present a new definition of local consistency, called relational consistency. The new definition is relationbased, in contrast with the previous definition of local consistency, which we characterize as variablebased. We show the conceptual power of the new definition by showing how it unifies known elimination operators such as resolution in theorem proving, joins in relational databases, and variable elimination for solving linear inequalities. Algorithms for enforcing various levels of relational consistency are introduced and analyzed. We also show the usefulness of the new definition in characterizing relationships between properties of constraint networks and the level of local consistency needed to ensure global consistency.
Constraint propagation
 Handbook of Constraint Programming
, 2006
"... Constraint propagation is a form of inference, not search, and as such is more ”satisfying”, both technically and aesthetically. —E.C. Freuder, 2005. Constraint reasoning involves various types of techniques to tackle the inherent ..."
Abstract

Cited by 51 (3 self)
 Add to MetaCart
Constraint propagation is a form of inference, not search, and as such is more ”satisfying”, both technically and aesthetically. —E.C. Freuder, 2005. Constraint reasoning involves various types of techniques to tackle the inherent
Ordering Heuristics for Arc Consistency Algorithms
 In AI/GI/VI ’92
, 1992
"... Arc consistency algorithms are used in solving constraint satisfaction problems and are important in constraint logic programming languages. Search order heuristics for arc consistency algorithms significantly enhance the efficiency of their implementation. In this paper we propose and evaluate seve ..."
Abstract

Cited by 48 (3 self)
 Add to MetaCart
Arc consistency algorithms are used in solving constraint satisfaction problems and are important in constraint logic programming languages. Search order heuristics for arc consistency algorithms significantly enhance the efficiency of their implementation. In this paper we propose and evaluate several ordering heuristics. Care is taken with experimental design, involving random problems, and statistical evaluation of results. A heuristic is identified which yields about 50% savings on average, using the standard measure of consistency pair checks, with reasonable heuristic computation cost. 1 Introduction Arc consistency insures that any two mutually constraining problem variables are mutually consistent: given a value for one, we can find a value for the other which satisfies the constraint between them. The constraint specifies which pairs of values can be simultaneously assumed by the pair of variables. Arc consistency is a fundamental concept in constraintbased reasoning [ Mack...