P. Van Hentenryck, H. Simonis, and M. Dincbas. Constraint satisfaction using constraint logic programming. Artificial Intelligence, 58:113--159, 1992.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....First, being integrated in a full CLP language, heuristics can be added in the program itself, as opposed to a closed boolean solver with (a nite set of) built in heuristics. Second, being integrated in a nite domains solver, various extensions such as pseudo booleans [2] or multi valued logics [24] can be integrated straightforwardly. Third, being based on a propagation method, searching for a single solution can be done much more quickly if the computation of all solutions is not needed. The rest of this paper is organized as follows. Section 2 introduces boolean constraints and the ....

....the propagation mechanisms that will be used to solve boolean constraints. We have indeed given the operational semantics of the constraint solver in this way. The most elegant way to implement such a solver would be to use some Ask primitive in a concurrent constraint language, as proposed by [24]. We do not have such a facility in clp(FD) and we will encode this propagation scheme by X in r constraints, as is detailed below. 4.3 Correctness and completeness of (B ; B ) It is important to ensure that our (operationally de ned) constraint system is equivalent to traditional boolean ....

P. Van Hentenryck, H. Simonis and M. Dincbas. Constraint Satisfaction Using Constraint Logic Programming. Arti cial Intelligence no 58, pp 113-159, 1992.

....fact, this approach was quite successful and it has become the standard tool in the commercial version of CHIP, whereas the specialpurpose boolean solver of CHIP (based on boolean uni cation) is optional. An important by product of this approach is that many extensions such as multi valued logics [49] or pseudo booleans (linear equations over booleans) 5] are available for free. In CHIP, the particular propagation scheme of the boolean and, or and not constraints is, following the black box approach, wired inside the solver and distinct from the nite domain part, although it uses some ....

P. Van Hentenryck, H. Simonis and M. Dincbas. Constraint Satisfaction Using Constraint Logic Programming. Arti cial Intelligence no 58, pp 113-159, 1992.

....(where n is the number of variables and d is the number of values) that is based on relating the gcc to ow theory. As well, a gcc can be rewritten as a collection of atleast and atmost constraints, one for each value, and constraint propagation can be performed on the individual constraints [14]. However, on some problems the rst technique su ers from its cubic run time and the second technique su ers from its lack of pruning power. An alternative which has not yet been explored with the gcc is bounds consistency propagation, a weaker form of consistency than domain consistency. Bounds ....

.... provides implementations of R egin s [8] domain consistency algorithm (denoted DC) and an algorithm (denoted CC) that enforces a level of consistency that is equivalent to enforcing domain consistency on individual cardinality constraints, where there is one cardinality constraint for each value [4, 14]. We compared the algorithms experimentally on various benchmark and random problems. All of the experiments were run on a 2.40 GHz Pentium 4 with 1 GB of main memory. Each reported runtime is the average of 10 runs except for random problems where 100 runs were performed. Unless otherwise noted, ....

P. Van Hentenryck, H. Simonis, and M. Dincbas. Constraint satisfaction using constraint logic programming. Arti cial Intelligence, 58:113-159, 1992.

....(where n is the number of variables and d is the number of values) that is based on relating the gcc to flow theory. As well, a gcc can be rewritten as a collection of atleast and atmost constraints, one for each value, and constraint propagation can be performed on the individual constraints [14]. However, on some problems the first technique su#ers from its cubic run time and the second technique su#ers from its lack of pruning power. An alternative which has not yet been explored with the gcc is bounds consistency propagation, a weaker form of consistency than domain consistency. Bounds ....

.... provides implementations of Regin s [8] domain consistency algorithm (denoted DC) and an algorithm (denoted CC) that enforces a level of consistency that is equivalent to enforcing domain consistency on individual cardinality constraints, where there is one cardinality constraint for each value [4, 14]. We compared the algorithms experimentally on various benchmark and random problems. All of the experiments were run on a 2.40 GHz Pentium 4 with 1 GB of main memory. Each reported runtime is the average of 10 runs except for random problems where 100 runs were performed. Unless otherwise noted, ....

P. Van Hentenryck, H. Simonis, and M. Dincbas. Constraint satisfaction using constraint logic programming. Artificial Intelligence, 58:113--159, 1992.

....equal to 0, can be replaced by the constraint that the output Z must be equal to 0. Hence, the goal and(0,Y,Z) will result in Z=0. These rules are the well known rules that can be found in several papers describing the propagation of boolean constraints, e.g. in form of demons [39] conditionals [99], CHR rules [45] or proof systems [36, 21] Our aim is to provide a method to generate such rules automatically provided the user speci es the right hand side of the rules to be a conjunction of equality constraints. Extension (Chapter 5) The operational semantics of CHR di ers from SLD ....

....and(X; Y; 1) X=1 Y =1: Now, the rst rule says that the constraint and(0; Y; Z) can be replaced by the equality constraint Z=0. These rules are the well known rules that can be found in several papers describing the propagation of boolean constraints, e.g. in form of demons [39] conditionals [99], CHR rules [46] or proof systems [36, 21] This chapter is organized as follows: In section 4.1, we present the algorithm for the generation of propagation rules and give some soundness, correctness and termination results. Then, we give more examples for the use of this algorithm. In section ....

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P. van Hentenryck, H. Simonis, and M. Dincbas. Constraint satisfaction using constraint logic programming. Arti cial Intelligence, 58(1-3), December 1992.

....Section 3.3 describes the benchmark sets used in the evaluation, and finally in Section 3.4 we discuss the results. 1 Chapter 2 The theory of NP(FD) 2. 1 Introduction Propagation algorithms have turned out to be quite powerful in proving theorems and solving combinatorial search problems [Hen89, HSD92b, JM94, DC93b, Car95] In particular, algorithms specialized to discrete constraint satisfaction problems, so called finite domain problems, have been carefully studied [HDT92] Similarly, propagation techniques tailored for propositional logic have been designed which compare favorably with other ....

P. Van Hentenryck, H. Simonis, and M. Dincbas. Constraint satisfaction using constraint logic programming. Artificial Intelligence, 58:113--159, 1992.

....Section 3.3 describes the benchmark sets used in the evaluation, and finally in Section 3.4 we discuss the results. 1 Chapter 2 The theory of NP(FD) 2. 1 Introduction Propagation algorithms have turned out to be quite powerful in proving theorems and solving combinatorial search problems [Hen89, HSD92b, JM94, DC93b, Car95] In particular, algorithms specialized to discrete constraint satisfaction problems, so called finite domain problems, have been carefully studied [HDT92] Similarly, propagation techniques tailored for propositional logic have been designed which compare favorably with other ....

P. Van Hentenryck, H. Simonis, and M. Dincbas. Constraint satisfaction using constraint logic programming. Artificial Intelligence, 58:113--159, 1992.

....sequenced together (see [14] for a detailed description of how these penalty values are calculated) For this representation, the optimization problem is defined as the minimization of penalty values. The Car Sequencing problem has been solved using a variety of techniques. Van Hentenryck et al. [22] used a constraint logic programming (CLP) approach on solvable problem instances (i.e. no optimization was required) The Car Sequencing problem was modeled with finite domains and in an arithmetic manner. The CLP language used takes advantage of this model by applying specialized finite domain, ....

P. Van Hentenryck, H. Simonis, and M. Dincbas. Constraint satisfaction using constraint logic programming. Department of Computer Science Technical Report CS-91-62, Brown University, Providence, RI, 1991.

....there has been little work reported specifically on the vehicle assembly line sequencing problem in the literature. Of the work that has been reported, most has focused on the specification of the vehicle assembly line sequencing problem introduced by Parrello et al. 6] Van Hentenryck et al. [10] and R egin and Puget [7] solve this version of the problem using backtracking search with specialized propagators to maintain arc consistency during the search. Local search techniques have also been developed for this version of the problem including a hill climbing approach [3] and a simulated ....

P. Van Hentenryck, H. Simonis, and M. Dincbas. Constraint satisfaction using constraint logic programming. Artificial Intelligence, 58:113--159, 1992.

....by enabling issues such as concurrence, control, and extensibility at the language level. This trend results in the language cc(FD) which is a successor to the finite domain part of CHIP. It was applied for solving two practical combinatorial problems, test pattern generation, and car sequencing [21]. The advantages of CLP technology over traditional techniques and, hence, the reasons for its industrial success lie in the next major points: a) declarative problem statement much closer to a natural one in which the programmer does not have to care about finding algorithms to solve a problem ....

Van Hentenryck, P., Simonis, H., and Dincbas, M., Constraint Satisfaction Using Constraint Logic Programming, Artif. Intell., 1992, vol. 58, nos. 1--3.

....there has been little work reported speci cally on the vehicle assembly line sequencing problem in the literature. Of the work that has been reported, most has focused on the speci cation of the vehicle assembly line sequencing problem introduced by Parrello et al. 6] Van Hentenryck et al. [10] and R egin and Puget [7] solve this version of the problem using backtracking search with specialized propagators to maintain arc consistency during the search. Local search techniques have also been developed for this version of the problem including a hill climbing approach [3] and a simulated ....

P. Van Hentenryck, H. Simonis, and M. Dincbas. Constraint satisfaction using constraint logic programming. Articial Intelligence, 58:113-159, 1992.

.... we have called timing analysis for synthesis (TAFS) in [47] Amon and Borriello [2] suggested a similar idea, that they call symbolic timing verification; however they studied only the convex case for which a solution can be given 143 using standard constraint satisfaction programming techniques [127, 68, 122], and failed to point out that in general symbolic verification results in a non convex problem. 5.2 Timing analysis for synthesis problem formulation Timing analysis for synthesis (TAFS) is a technique that can be used in advance of the interface logic synthesis to determine the values that ....

P. Van Hentenryck, H. Simonis, and M. Dincbas, "Constraint satisfaction using constraint logic programming," Artificial Intelligence, vol. 58, pp. 113--159, 1992.

.... Solving of CSPs being a NP hard problem, several approximations of the solution space, computed by local consistency methods have been proposed, the most famous being arc consistency [19] and path consistency [23] Since the introduction of local consistency in Constraint Logic Programming [29] various extensions have been proposed, among which methods to solve socalled interval constraints [10, 8, 26, 15, 14, 5, 18, 3, 4, 28, 27] More recently several authors have studied various combinations of solvers in the case of continuous real constraints [20, 22, 13] and, in particular, ....

P. Van Hentenryck, H. Simonis, and M. Dincbas. Constraint Satisfaction Using Constraint Logic Programming. Artificial Intelligence, 58(1-3):113--159, Dec. 1992.

.... analysis [1] however, it is unusual to incorporate arithmetic expressions in the set constraint language and solver (but see [28] for an important partial exception) 11 Note also that techniques for solving integer constraint systems may be found in the artificial intelligence literature [14, 32, 37, 58]; however, their algorithms typically stress generality for small problems ( hundreds of nodes and constraints [14] over scalability and thus are not directly applicable here. LINT LIKE TOOLS. Several commonly used tools [34, 18, 19] use static analysis and some heuristics to detect common ....

P. Van Hentenryck, H. Simonis, M. Dincbas, "Constraint satisfaction using constraint logic programming," Artificial Intelligence, vol.58, 1992, pp.113--159.

....as follows. We cover first general state models (Sect. 2) and the state models underlying problems expressed in Strips (Sect. 3) We then present a domain independent 1 Interestingly, the area of constraint programminghas similar goals although it is focused on a different class of problems [HSD92]. Yet, see [BC99] for a recent attempt to apply the ideas of constraint programming in planning. 2 Another way to reduce the gap between planners and specialized solvers is by making room in planning languages for expressing domain dependent control knowledge (e.g. BK98] In this paper, ....

P. Van Hentenryck, H. Simonis, and M. Dincbas. Constraint satisfaction using constraint logic programming. Artificial Intelligence, 58(1--3):113--159, 1992.

....fact, this approach was quite successful and it has become the standard tool in the commercial version of CHIP, whereas the specialpurpose boolean solver of CHIP (based on boolean unification) is optional. An important by product of this approach is that many extensions such as multi valued logics [49] or pseudo booleans (linear equations over booleans) 5] are available for free. In CHIP, the particular propagation scheme of the boolean and, or and not constraints is, following the black box approach, wired inside the solver and distinct from the finite domain part, although it uses some ....

P. Van Hentenryck, H. Simonis and M. Dincbas. Constraint Satisfaction Using Constraint Logic Programming. Artificial Intelligence no 58, pp 113-159, 1992.

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P. Van Hentenryck, H. Simonis, and M. Dincbas. Constraint satisfaction using constraint logic programming. Artificial Intelligence, 58:113--159, 1992.

No context found.

P. Van Hentenryck, H. Simonis, and M. Dincbas. Constraint satisfaction using constraint logic programming. Artificial Intelligence, 58(1-3):113--159, December 1992.

No context found.

P. Van Hentenryck, H. Simonis, and M. Dincbas. Constraint satisfaction using constraint logic programming. Artificial Intelligence, 58:113--159, 1992.

No context found.

P. Van Hentenryck, H. Simonis and M. Dincbas, "Constraint Satisfaction using Constraint Logic Programming", Artificial Intelligence, vol. 58, pp. 113-159, 1992.

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P. Van Hentenryck, H. Simonis and M. Dincbas. Constraint Satisfaction Using Constraint Logic Programming. Arti cial Intelligence no 58, pp 113-159, 1992.

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P. Van Hentenryck, H. Simonis, and M. Dincbas. Constraint satisfaction using constraint logic programming. Arti cial Intelligence, no 58:pp 113-159, 1992.

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