Constraint Logic Programming

Abstract

Abstract. Constraint Logic Programming (CLP) is one of the most successful branches of Logic Programming; it attracts the interest of theoreticians and practitioners, and it is currently used in many commercial applications. Since the original proposal, it has developed enormously: many languages and systems are now available either as open source programs or as commercial systems.Also, CLP has been one of the technologies able to recruit researchers from other communities to the declarative programming cause. Current CLP engines include technologies and results developed in other communities, which themselves discovered logic as an invaluable tool to model and solve real-life problems. The CLP ParadigmConstraint Logic Programming (CLP) [7] represents a successful attempt to merge the best features of logic programming (LP) and constraint solving.Constraint solving [127,6,56,31] includes a variety of expressive modelling frameworks and efficient solving tools for real-life problems that can be described via a set of variables and constraints over them. A constraint is just a restriction imposed over the combination of values of some variables of the problem. Solving a problem with constraints means finding a way to assign values to all its variables such that all constraints are satisfied. Constraint solving methods have been successfully applied to many application domains, such as scheduling, planning, resource allocation, vehicle routing, computer networks, and bioinformatics [137,127,51].Embedding the notion of constraint into a high-level programming language allows for a more flexible and practical constraint processing environment, where constraints can be represented as formulae and can be incrementally accumulated. Moreover, the presence of constraints in a programming language usually augments its expressive power, in the sense that some complex relations can be defined easily by means of constraints, and there are also efficient techniques to prove them.For these reasons, constraints have been embedded in many programming environments, but some are more suitable than others. For example, the fact that constraints can be seen as relations or predicates, that constraint solving can be seen as a generalized form of unification, that their conjunction can be seen as a logical and, and that backtracking search is the base methodology to A. Dovier, E. Pontelli (Eds.): 25

Concurrent and Reactive Constraint Programming

“…As described in more detail in another chapter of this book [40], soft constraints extend classical constraints to represent multiple consistency levels, and thus provide a way to express preferences, fuzziness, and uncertainty. An extension of the ccp framework which allows soft constraints in the calculus has been proposed in [6].…”

Conceptual Requirement Validation for Architecture Design Systems

“…Building new constraint solvers is laborious because it typically requires hardwiring the constraint solver into the complier or interpreter [25]. The use of Constraint Handling Rules (CHR) can help this process by providing a high level programming framework designed for the implementation and integrate constraint solvers into logic based languages.…”

“…In this regard, a logic programing language relies on built-in search and unification algorithms to solve logic based formula. At its essence, logic programming can be viewed as a basic form of constraint solving [25].…”

“…Constraint Logic Programming (CLP) combines logic programing with constraint solving [25] [42] [27]. Logic programming is based on a declarative semantic in which programs are defined in terms of what should be computed instead of how it should be computed (i.e.…”