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DESIGN, IMPLEMENTATION, AND EVALUATION OF THE CONSTRAINT LANGUAGE cc(FD)
 J. LOGIC PROGRAMMING 1994:19, 20:1679
, 1994
"... This paper describes the design, implementation, and applications of the constraint logic language cc(FD). cc(FD) is a declarative nondeterministic constraint logic language over finite domains based on the cc framework [33], an extension of the CLP scheme [21]. Its constraint solver includes (nonl ..."
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Cited by 166 (9 self)
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This paper describes the design, implementation, and applications of the constraint logic language cc(FD). cc(FD) is a declarative nondeterministic constraint logic language over finite domains based on the cc framework [33], an extension of the CLP scheme [21]. Its constraint solver includes (nonlinear) arithmetic constraints over natural numbers which are approximated using domain and interval consistency. The main novelty of cc(FD) is the inclusion of a number of generalpurpose combinators, in particular cardinality, constructive disjunction, and blocking implication, in conjunction with new constraint operations such as constraint entailment and generalization. These combinators significantly improve the operational expressiveness, extensibility, and flexibility of CLP languages and allow issues such as the definition of nonprimitive constraints and disjunctions to be tackled at the language level. The implementation of cc(FD) (about 40,000 lines of C) includes a WAMbased engine [44], optimal arcconsistency algorithms based on AC5 [40], and incremental implementation of the combinators. Results on numerous problems, including scheduling, resource allocation, sequencing, packing, and hamiltonian paths are reported and indicate that cc(FD) comes close to procedural languages on a number of combinatorial problems. In addition, a small cc(FD) program was able to find the optimal solution and prove optimality to a famous 10/10 disjunctive scheduling problem [29], which was left open for more than 20 years and finally solved in 1986.
Constraint Hierarchies
 LISP AND SYMBOLIC COMPUTATION
, 1992
"... Constraints allow programmers and users to state declaratively a relation that should be maintained, rather than requiring them to write procedures to maintain the relation themselves. They are thus useful in such applications as programming languages, user interface toolkits, and simulation package ..."
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Cited by 144 (14 self)
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Constraints allow programmers and users to state declaratively a relation that should be maintained, rather than requiring them to write procedures to maintain the relation themselves. They are thus useful in such applications as programming languages, user interface toolkits, and simulation packages. In many situations, it is desirable to be able to state both required and preferential constraints. The required constraints must hold. Since the other constraints are merely preferences, the system should try to satisfy them if possible, but no error condition arises if it cannot. A constraint hierarchy consists of a set of constraints, each labeled as either required or preferred at some strength. An arbitrary number of different strengths is allowed. In the discussion of a theory of constraint hierarchies, we present alternate ways of selecting among competing possible solutions, and prove a number of propositions about the relations among these alternatives. We then outline algorit...
Models and Languages for Parallel Computation
 ACM COMPUTING SURVEYS
, 1998
"... We survey parallel programming models and languages using 6 criteria [:] should be easy to program, have a software development methodology, be architectureindependent, be easy to understand, guranatee performance, and provide info about the cost of programs. ... We consider programming models in ..."
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Cited by 136 (4 self)
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We survey parallel programming models and languages using 6 criteria [:] should be easy to program, have a software development methodology, be architectureindependent, be easy to understand, guranatee performance, and provide info about the cost of programs. ... We consider programming models in 6 categories, depending on the level of abstraction they provide.
Practical Applications of Constraint Programming
 CONSTRAINTS
, 1996
"... Constraint programming is newly flowering in industry. Several companies have recently started up to exploit the technology, and the number of industrial applications is now growing very quickly. This survey will seek, by examples, ..."
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Cited by 106 (1 self)
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Constraint programming is newly flowering in industry. Several companies have recently started up to exploit the technology, and the number of industrial applications is now growing very quickly. This survey will seek, by examples,
The Essence of Constraint Propagation
 CWI QUARTERLY VOLUME 11 (2&3) 1998, PP. 215 { 248
, 1998
"... We show that several constraint propagation algorithms (also called (local) consistency, consistency enforcing, Waltz, ltering or narrowing algorithms) are instances of algorithms that deal with chaotic iteration. To this end we propose a simple abstract framework that allows us to classify and comp ..."
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Cited by 90 (6 self)
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We show that several constraint propagation algorithms (also called (local) consistency, consistency enforcing, Waltz, ltering or narrowing algorithms) are instances of algorithms that deal with chaotic iteration. To this end we propose a simple abstract framework that allows us to classify and compare these algorithms and to establish in a uniform way their basic properties.
Multiway versus Oneway Constraints in User Interfaces: Experience with the DeltaBlue Algorithm
, 1993
"... this paper we argue that many user interface construction problems are handled more naturally and elegantly by multiway constraints than by oneway constraints. We present pseudocode for an incremental multiway constraint satisfaction algorithm, DeltaBlue, and describe experience in using the algo ..."
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Cited by 83 (17 self)
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this paper we argue that many user interface construction problems are handled more naturally and elegantly by multiway constraints than by oneway constraints. We present pseudocode for an incremental multiway constraint satisfaction algorithm, DeltaBlue, and describe experience in using the algorithm in two user interface toolkits. Finally, we provide performance figures demonstrating that multiway constraint solvers can be entirely competitive in performance with oneway constraint solvers
Constraint satisfaction using constraint logic programming
 Artificial Intelligence
, 1992
"... Constraint logic programming (CLP) is a new class of declarative programming languages whose primitive operations are based on constraints (e.g. constraint solving and constraint entailment). CLP languages naturally combine constraint propagation with nondeterministic choices. As a consequence, the ..."
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Cited by 74 (3 self)
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Constraint logic programming (CLP) is a new class of declarative programming languages whose primitive operations are based on constraints (e.g. constraint solving and constraint entailment). CLP languages naturally combine constraint propagation with nondeterministic choices. As a consequence, they are particularly appropriate for solving a variety of combinatorial search problems, using the global search paradigm, with short development time and efficiency comparable to procedural tools based on the same approach. In this paper, we describe how the CLP language cc(FD), a successor of CHIP using consistency techniques over finite domains, can be used to solve two practical applications: testpattern generation and car sequencing. For both applications, we present the cc(FD) program, describe how constraint solving is performed, report experimental results, and compare the approach with existing tools.
Constraint processing in cc(FD)
, 1992
"... Constraint logic programming languages such as CHIP [26,5] have demonstrated the practicality of declarative languages supporting consistency techniques and nondeterminism. Nevertheless they suffer from the blackbox effect: the programmer must work with a monolithic, unmodifiable, inextensible cons ..."
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Cited by 67 (11 self)
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Constraint logic programming languages such as CHIP [26,5] have demonstrated the practicality of declarative languages supporting consistency techniques and nondeterminism. Nevertheless they suffer from the blackbox effect: the programmer must work with a monolithic, unmodifiable, inextensible constraintsolver. This problem can be overcome within the logically and computationally richer concurrent constraint (cc) programming paradigm [17]. We show that some basic constraintoperations currently hardwired into constraintsolvers can be abstracted and made available as combinators in the programming language. This allows complex constraintsolvers to be decomposed into logically clean and efficiently implementable cc programs over a much simpler constraint system. In particular, we show that the CHIP constraintsolver can be simply programmed in cc(FD), acc language with an extremely simple builtin constraint solver for finite domains.
Hierarchical Constraint Logic Programming
, 1993
"... A constraint describes a relation to be maintained ..."
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Cited by 67 (3 self)
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A constraint describes a relation to be maintained
Temporal Concurrent Constraint Programming: Denotation, Logic and Applications
, 2002
"... The tcc model is a formalism for reactive concurrent constraint programming. We present a model of temporal concurrent constraint programming which adds to tcc the capability of modeling asynchronous and nondeterministic timed behavior. We call this tcc extension the ntcc calculus. We also give a d ..."
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Cited by 66 (23 self)
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The tcc model is a formalism for reactive concurrent constraint programming. We present a model of temporal concurrent constraint programming which adds to tcc the capability of modeling asynchronous and nondeterministic timed behavior. We call this tcc extension the ntcc calculus. We also give a denotational semantics for the strongestpostcondition of ntcc processes and, based on this semantics, we develop a proof system for lineartemporal properties of these processes. The expressiveness of ntcc is illustrated by modeling cells, timed systems such as RCX controllers, multiagent systems such as the Predator /Prey game, and musical applications such as generation of rhythms patterns and controlled improvisation. 1