scholarly journals Adding partial functions to Constraint Logic Programming with sets

2015 ◽  
Vol 15 (4-5) ◽  
pp. 651-665 ◽  
Author(s):  
MAXIMILIANO CRISTIÁ ◽  
GIANFRANCO ROSSI ◽  
CLAUDIA FRYDMAN

AbstractPartial functions are common abstractions in formal specification notations such as Z, B and Alloy. Conversely, executable programming languages usually provide little or no support for them. In this paper we propose to add partial functions as a primitive feature to a Constraint Logic Programming (CLP) language, namely {log}. Although partial functions could be programmed on top of {log}, providing them as first-class citizens adds valuable flexibility and generality to the form of set-theoretic formulas that the language can safely deal with. In particular, the paper shows how the {log} constraint solver is naturally extended in order to accommodate for the new primitive constraints dealing with partial functions. Efficiency of the new version is empirically assessed by running a number of non-trivial set-theoretical goals involving partial functions, obtained from specifications written in Z.

1991 ◽  
Vol 6 (3) ◽  
pp. 151-194 ◽  
Author(s):  
Pascal Van Hentenryck

AbstractConstraint logic programming (CLP) is a generalization of logic programming (LP) where unification, the basic operation of LP languages, is replaced by constraint handling in a constraint system. The resulting languages combine the advantages of LP (declarative semantics, nondeterminism, relational form) with the efficiency of constraint-solving algorithms. For some classes of combinatorial search problems, they shorten the development time significantly while preserving most of the efficiency of imperative languages. This paper surveys this new class of programming languages from their underlying theory, to their constraint systems, and to their applications to combinatorial problems.


1998 ◽  
Vol 07 (04) ◽  
pp. 453-462
Author(s):  
CRISTINA FIERBINTEANU

In this paper we propose a model of a decision support systems (DSS) generator for unstructured problems. The model is developed within the constraint logic programming (CLP) paradigm. At the center of the generator there is an ontology defining the concepts and relationships necessary and sufficient to describe the domain to be reasoned about, in a manner suitable for a particular class of tasks. The constraint solver of the constraint logic programming host language has to be extended with constraints which are relevant to the domain studied, but can not be found among the general constraints provided by the constraint solver. The domain of transportation planning was chosen to illustrate the proposed concept of DSS generator for ustructured problems. In this case we need to extend the constraint solver with constraint manipulation techniques specific to network flow problems. This paper presents in detail our constraint logic programming approach of network flow problems.


2009 ◽  
Vol 9 (6) ◽  
pp. 691-750 ◽  
Author(s):  
PAOLO MANCARELLA ◽  
GIACOMO TERRENI ◽  
FARIBA SADRI ◽  
FRANCESCA TONI ◽  
ULLE ENDRISS

AbstractWe present the CIFF proof procedure for abductive logic programming with constraints, and we prove its correctness. CIFF is an extension of the IFF proof procedure for abductive logic programming, relaxing the original restrictions over variable quantification (allowedness conditions) and incorporating a constraint solver to deal with numerical constraints as in constraint logic programming. Finally, we describe the CIFF system, comparing it with state-of-the-art abductive systems and answer set solvers and showing how to use it to program some applications.


Sign in / Sign up

Export Citation Format

Share Document