Results 1  10
of
112
Partial Constraint Satisfaction
, 1992
"... . A constraint satisfaction problem involves finding values for variables subject to constraints on which combinations of values are allowed. In some cases it may be impossible or impractical to solve these problems completely. We may seek to partially solve the problem, in particular by satisfying ..."
Abstract

Cited by 471 (21 self)
 Add to MetaCart
. A constraint satisfaction problem involves finding values for variables subject to constraints on which combinations of values are allowed. In some cases it may be impossible or impractical to solve these problems completely. We may seek to partially solve the problem, in particular by satisfying a maximal number of constraints. Standard backtracking and local consistency techniques for solving constraint satisfaction problems can be adapted to cope with, and take advantage of, the differences between partial and complete constraint satisfaction. Extensive experimentation on maximal satisfaction problems illuminates the relative and absolute effectiveness of these methods. A general model of partial constraint satisfaction is proposed. 1 Introduction Constraint satisfaction involves finding values for problem variables subject to constraints on acceptable combinations of values. Constraint satisfaction has wide application in artificial intelligence, in areas ranging from temporal r...
A sufficient condition for backtrackfree search
, 1982
"... A constraint satisfaction problem revolves finding values for a set of variables subject of a set of constraints (relations) on those variables Backtrack search is often used to solve such problems. A relationship involving the structure of the constraints i described which characterizes tosome deg ..."
Abstract

Cited by 288 (14 self)
 Add to MetaCart
A constraint satisfaction problem revolves finding values for a set of variables subject of a set of constraints (relations) on those variables Backtrack search is often used to solve such problems. A relationship involving the structure of the constraints i described which characterizes tosome degree the extreme case of mimmum backtracking (none) The relationship involves a concept called "width," which may provide some guidance in the representation f constraint satisfaction problems and the order m which they are searched The width concept is studied and applied, in particular, to constraints which form tree structures.
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 228 (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...
Configuration as Composite Constraint Satisfaction
 in Proc. Artificial Intelligence and Manufacturing. Research Planning Workshop
, 1996
"... Selecting and arranging parts is the core of a configuration task. The validity of a configuration is defined in terms of constraints. Highly declarative, domain independent and simple to use, the constraint satisfaction problem (CSP) paradigm offers an adequate framework for this task. However, the ..."
Abstract

Cited by 87 (4 self)
 Add to MetaCart
Selecting and arranging parts is the core of a configuration task. The validity of a configuration is defined in terms of constraints. Highly declarative, domain independent and simple to use, the constraint satisfaction problem (CSP) paradigm offers an adequate framework for this task. However, the basic paradigm is not powerful enough to capture or to take advantage of essential aspects of configuration, such as the unknown a priori number of constituent parts of a system or the inherent internal structure of these parts. Although notable effort has been spent on extending the basic paradigm to accommodate these issues, we still lack a comprehensive formalism for configuration. This paper presents the main ideas behind a general constraintbased model of configuration tasks represented as a new class of nonstandard constraint satisfaction problems, called composite CSP. Composite CSP unifies several CSP extensions, providing a more comprehensive and efficient basis for formulating and solving configuration problems.
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 78 (5 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.
Taking advantage of stable sets of variables in constraint satisfaction problems
 In IJCAI’85
, 1985
"... Binary constraint satisfaction problems involve finding values for variables subject to constraints between pairs of variables. Algorithms that take advantage of the structure of constraint connections can be more efficient than simple backtrack search. Some pairs of variables may have no direct con ..."
Abstract

Cited by 76 (0 self)
 Add to MetaCart
Binary constraint satisfaction problems involve finding values for variables subject to constraints between pairs of variables. Algorithms that take advantage of the structure of constraint connections can be more efficient than simple backtrack search. Some pairs of variables may have no direct constraint between them, even if they are linked indirectly through a chain of constraints involving other variables. A set of variables with no direct constraint between any pair of them forms a stable set in a constraint graph representation of a problem. We describe an algorithm designed to take advantage of stable sets of variables, and give experimental evidence that it can outperform not only simple backtracking, but also forward checking, one of the best variants of backtrack search. Potential applications to parallel processing are noted. Some light is shed on the question of how and when a constraint satisfaction problem can be advantageously divided
Using Inference to Reduce Arc Consistency Computation
 PROCEEDINGS OF THE FOURTEENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI’95
, 1995
"... Constraint satisfaction problems are widely used in artificial intelligence. They involve finding values for problem variables subject to constraints that specify which combinations of values are consistent. Knowledge about properties of the constraints can permit inferences that reduce the co ..."
Abstract

Cited by 65 (12 self)
 Add to MetaCart
Constraint satisfaction problems are widely used in artificial intelligence. They involve finding values for problem variables subject to constraints that specify which combinations of values are consistent. Knowledge about properties of the constraints can permit inferences that reduce the cost of consistency checking. In particular, such inferences can be used to reduce the number of constraint checks required in establishing arc consistency, a fundamental constraintbased reasoning technique. A general ACInference schema is presented and various forms of inference discussed. A specific algorithm, AC7, is presented, which takes advantage of a simple property common to all binary constraints to eliminate constraint checks that other arc consistency algorithms perform. The effectiveness of this approach is demonstrated analytically, and experimentally on realworld problems.
Using Constraint Metaknowledge to Reduce Arc Consistency Computation
 Artificial Intelligence
, 1999
"... Constraint satisfaction problems are widely used in articial intelligence. They involve nding values for problem variables subject to constraints that specify which combinations of values are consistent. Knowledge about properties of the constraints can permit inferences that reduce the cost of cons ..."
Abstract

Cited by 61 (8 self)
 Add to MetaCart
Constraint satisfaction problems are widely used in articial intelligence. They involve nding values for problem variables subject to constraints that specify which combinations of values are consistent. Knowledge about properties of the constraints can permit inferences that reduce the cost of consistency checking. In particular, such inferences can be used to reduce the number of constraint checks required in establishing arc consistency, a fundamental constraintbased reasoning technique. A general ACInference algorithm schema is presented and various forms of inference discussed. A specific algorithm, AC7, is presented, which takes advantage of a simple property common to all binary constraints to eliminate constraint checks that other arc consistency algorithms perform. The effectiveness of this approach is demonstrated analytically, and experimentally.
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 51 (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...
Understanding and Improving the MAC Algorithm
 In Third International Conference on Principles and Practice of Constraint Programming, LNCS 1330
, 1997
"... . 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. Recent research on finite domain constraint satisf ..."
Abstract

Cited by 44 (2 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. Recent research on finite domain constraint satisfaction problems suggest that Maintaining Arc Consistency (MAC) is the most efficient general CSP algorithm for solving large and hard problems. In the first part of this paper we explain why maintaining full, as opposed to limited, arc consistency during search can greatly reduce the search effort. Based on this explanation, in the second part of the paper we show how to modify MAC in order to make it even more efficient. Experimental results prove that the gain in efficiency can be quite important. 1 Introduction Constraint satisfaction problems (CSPs) involve finding values for problem variables subject to constraints that are restrictions on which combinations of values are allowed. They...
Results 1  10
of
112