A detailed algorithm testing guards over feature trees

Artificial Intelligence, 1992

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

Principles and Practice of Constraint Programming – CP’99, 1999

We study here constraint satisfaction problems that are based on predefined, explicitly given finite constraints. To solve them we propose a notion of rule consistency that can be expressed in terms of rules derived from the explicit representation of the initial constraints. This notion of local consistency is weaker than arc consistency for constraints of arbitrary arity but coincides with it when all domains are unary or binary. For Boolean constraints rule consistency coincides with the closure under the wellknown propagation rules for Boolean constraints. By generalizing the format of the rules we obtain a characterization of arc consistency in terms of so-called inclusion rules. The advantage of rule consistency and this rule based characterization of the arc consistency is that the algorithms that enforce both notions can be automatically generated, as CHR rules. So these algorithms could be integrated into constraint logic programming systems such as ECL;PS 0 • We illustrate the usefulness of this approach to constraint propagation by discussing the implementations of both algorithms and their use on various examples, including Boolean constraints, three valued logic of Kleene, constraints dealing with Waltz's language for describing polyhedreal scenes, and Allen's qualitative approach to temporal logic.

Proceedings of the 20th ACM SIGPLAN-SIGACT symposium on Principles of programming languages - POPL '93, 1993

Central to constraint logic programming (CLP) languages is the notion of a global constraint solver which is queried to direct execution and to which constraints are monotonically added. We present a methodology for use in the compilation of CLP languages which is designed to reduce the overhead of the global constraint solver. This methodology is based on three optimizations. The first, refinement, involves adding new constraints, which in effect make information available earlier in the computation, guiding subsequent execution away from unprofitable choices. The second, removal, involves eliminating constraints from the solver when they are redundant. The last, reordering, involves moving constraint addition later and constraint remowd earlier in the computation. Determining the applicability of each optimization requires sophisticated global analysis. These analyses are based on abstract interpretation and provide information about potential and definite interaction between constraints.

The Journal of Logic Programming, 1998

The Constraint Logic Programming (CLP) Scheme was introduced by Jaar and Lassez. The scheme gave a formal framework, based on constraints, for the basic operational, logical and algebraic semantics of an extended class of logic programs. This paper presents for the ®rst time the semantic foundations of CLP in a self-contained and complete package. The main contributions are threefold. First, we extend the original conference paper by presenting de®nitions and basic semantic constructs from ®rst principles, giving new and complete proofs for the main lemmas. Importantly, we clarify which theorems depend on conditions such as solution compactness, satisfaction completeness and independence of constraints. Second, we generalize the original results to allow for incompleteness of the constraint solver. This is important since almost all CLP systems use an incomplete solver. Third, we give conditions on the (possibly incomplete) solver which ensure that the operational semantics is con¯uent, that is, has independence of literal scheduling.

2003

This book constitutes the thoroughly refereed post-proceedings of the Joint ERCIM/CologNet International Workshop on Constraint Solving and Constraint Logic Programming, held in Cork, Ireland in June 2002. The 14 revised full papers presented were carefully selected for inclusion in the book during two rounds of reviewing and revision. Among the topics addressed are verification and debugging of constraint logic programs, modeling and solving CSPs, explanation generation, inference and inconsistency ...

2009

Product configuration is a major industrial application domain for constraint satisfaction techniques. Conditional constraint satisfaction problems (CCSPs) and feature models (FMs) have been developed to represent configuration problems in a natural way. CCSPs are like constraint satisfaction problems (CSPs), but they also include potential variables, which might or might not exist in any given solution, as well as classical variables, which are required to take a value in every solution. CCSPs model, for example, options on a car, for which the style of sunroof (a variable) only makes sense if the car has a sunroof at all. FMs are directed acyclic graphs of features with constraints on edges. FMs model, for example, cell phone features, where utility functions are required, but the particular utility function "games" is optional, but requires Java support. We show that existing techniques from formal methods and answer set programming can be used to naturally model CCSPs and FMs. We demonstrate configurators in both approaches. An advantage of these approaches is that the model builder does not have to reformulate the CCSP or FM into a classic CSP, converting potential variables into classical variables by adding a "does not exist" value and modifying the problem constraints. Our configurators automatically reason about the model itself, enumerating all solutions and discovering several kinds of model flaws.

Journal of Logic Programming, 1998

The Constraint Logic Programming (CLP) Scheme was introduced by Jaar and Lassez. The scheme gave a formal framework, based on constraints, for the basic operational, logical and algebraic semantics of an extended class of logic programs. This paper presents for the ®rst time the semantic foundations of CLP in a self-contained and complete package. The main contributions are threefold. First, we extend the original conference paper by presenting de®nitions and basic semantic constructs from ®rst principles, giving new and complete proofs for the main lemmas. Importantly, we clarify which theorems depend on conditions such as solution compactness, satisfaction completeness and independence of constraints. Second, we generalize the original results to allow for incompleteness of the constraint solver. This is important since almost all CLP systems use an incomplete solver. Third, we give conditions on the (possibly incomplete) solver which ensure that the operational semantics is con¯uent, that is, has independence of literal scheduling. Ó 1998 Elsevier Science Inc. All rights reserved.

Artificial intelligence for engineering design, analysis and manufacturing, 2011

Product configuration is a major industrial application domain for constraint satisfaction techniques. Conditional constraint satisfaction problems (CCSPs) and feature models (FMs) have been developed to represent configuration problems in a natural way. CCSPs are like constraint satisfaction problems (CSPs), but they also include potential variables, which might or might not exist in any given solution, as well as classical variables, which are required to take a value in every solution. CCSPs model, for example, options on a car, for which the style of sunroof (a variable) only makes sense if the car has a sunroof at all. FMs are directed acyclic graphs of features with constraints on edges. FMs model, for example, cell phone features, where utility functions are required, but the particular utility function "games" is optional, but requires Java support. We show that existing techniques from formal methods and answer set programming can be used to naturally model CCSPs and FMs. We demonstrate configurators in both approaches. An advantage of these approaches is that the model builder does not have to reformulate the CCSP or FM into a classic CSP, converting potential variables into classical variables by adding a "does not exist" value and modifying the problem constraints. Our configurators automatically reason about the model itself, enumerating all solutions and discovering several kinds of model flaws.

Constraints

This paper compares the efficiency of a number of Constraint Logic Programming (CLP) systems in the setting of finite domains as well as a specific aspect of their expressiveness (that concerning reification and meta-constraints). There are two key reasons for adopting CLP technology for solving a problem. The first is its expressiveness enabling a declarative solution with readable code which is vital for maintenance and the second is the provision of an efficient implementation for the computationally expensive procedures. However, CLP systems differ significantly both in how solutions may be expressed and the efficiency of their execution and it is important that both these factors are taken into account when choosing the best CLP system for a particular application. This paper aids this choice by illustrating differences between the systems, indicating their particular strengths and weaknesses.