Records for logic programming

Smolka, Gert; Treinen, Ralf

doi:10.1016/0743-1066(94)90044-2

Cited by 82 publications

(69 citation statements)

References 12 publications

(23 reference statements)

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“…Most automatic and semi-automatic tools for proving program termination 3 and for complexity analysis 4 agree on the fact that list/1 will terminate when invoked with a ground argument. Consider now the query…”

Section: List([]) List([ |T]) :-List(t)mentioning

confidence: 99%

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Finite-Tree Analysis for Constraint Logic-Based Languages

et al. 2001

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Logic languages based on the theory of rational, possibly infinite, trees have much appeal in that rational trees allow for faster unification (due to the safe omission of the occurs-check) and increased expressivity (cyclic terms can provide very efficient representations of grammars and other useful objects). Unfortunately, the use of infinite rational trees has problems. For instance, many of the built-in and library predicates are ill-defined for such trees and need to be supplemented by run-time checks whose cost may be significant. Moreover, some widely-used program analysis and manipulation techniques are correct only for those parts of programs working over finite trees. It is thus important to obtain, automatically, a knowledge of the program variables (the finite variables) that, at the program points of interest, will always be bound to finite terms. For these reasons, we propose here a new dataflow analysis, based on abstract interpretation, that captures such information. We present a parametric domain where a simple component for recording finite variables is coupled, in the style of the open product construction of Cortesi et al., with a generic domain (the parameter of the construction) providing sharing information. The sharing domain is abstractly specified so as to guarantee the correctness of the combined domain and the generality of the approach. This finite-tree analysis domain is further enhanced by coupling it with a domain of Boolean functions, called finite-tree dependencies, that precisely captures how the finiteness of some variables influences the finiteness of other variables. We also summarize our experimental results showing how finite-tree analysis, enhanced with finite-tree dependencies, is a practical means of obtaining precise finiteness information.

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Section: List([]) List([ |T]) :-List(t)mentioning

confidence: 99%

“…Other (constraint) logic-based languages, such as Prolog II and its successors [1,2], SICStus Prolog [3], and Oz [4], refer to a computation domain of rational trees.…”

Section: Introductionmentioning

confidence: 99%

Finite-Tree Analysis for Constraint Logic-Based Languages

et al. 2001

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“…Oz 1 features a new record data type that was inspired by LIFE (Smolka & Treinen, 1994;Van Roy et al, 1996). Concurrency in Oz 1 is implicit and based on lazy thread creation.…”

Section: Concurrent Constraintsmentioning

confidence: 99%

Logic programming in the context of multiparadigm programming: the Oz experience

Roy¹,

Brand²,

Duchier³

et al. 2003

Theory and Practice of Logic Programming

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Oz is a multiparadigm language that supports logic programming as one of its major paradigms. A multiparadigm language is designed to support different programming paradigms (logic, functional, constraint, object-oriented, sequential, concurrent, etc.) with equal ease. This article has two goals: to give a tutorial of logic programming in Oz and to show how logic programming fits naturally into the wider context of multiparadigm programming. Our experience shows that there are two classes of problems, which we call algorithmic and search problems, for which logic programming can help formulate practical solutions. Algorithmic problems have known efficient algorithms. Search problems do not have known efficient algorithms but can be solved with search. The Oz support for logic programming targets these two problem classes specifically, using the concepts needed for each. This is in contrast to the Prolog approach, which targets both classes with one set of concepts, which results in less than optimal support for each class. We give examples that can be run interactively on the Mozart system, which implements Oz. To explain the essential difference between algorithmic and search programs, we define the Oz execution model. This model subsumes both concurrent logic programming (committed-choice-style) and search-based logic programming (Prolog-style). Furthermore, as consequences of its multiparadigm nature, the model supports new abilities such as first-class top levels, deep * This article is a much-extended version of the tutorial talk "Logic Programming in Oz withMozart" given at the International Conference on Logic Programming, Las Cruces, New Mexico, Nov. 1999. Some knowledge of traditional logic programming (with Prolog or concurrent logic languages) is assumed. guards, active objects, and sophisticated control of the search process. Instead of Horn clause syntax, Oz has a simple, fully compositional, higher-order syntax that accommodates the abilities of the language. We give a brief history of Oz that traces the development of its main ideas and we summarize the lessons learned from this work. Finally, we give many entry points into the Oz literature.

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“…For instance, if AxCFT is Smolka and Treinen's axiomatization of the domain of feature trees [16], the constraint system CFT can be defined considering the whole set of first-order formulas as constraints, and Γ CF T C iff Γ ∪ AxCFT C, where is the entailment relation of classical first-order logic with equality. Another example is the constraint system R that can be defined analogously to CFT , but using AxR, the Tarski's axiomatization of the closed field of real numbers [17].…”

Section: Constraint Systemsmentioning

confidence: 99%

Providing declarative semantics for HH extended constraint logic programs

García‐Díaz

Nieva

2004

Proceedings of the 6th ACM SIGPLAN International Conference on Principles and Practice of Declarative Programming

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This paper is focused on a double extension of traditional Logic Programming which enhances it following two different approaches. On one hand, extending Horn logic to hereditary Harrop formulas (HH ), in order to improve the expressive power; on the other, incorporating constraints, in order to increase the efficiency. For this combination, called HH(C), an operational semantics exists, but no declarative semantic for it has been defined so far.One of the main features of (Constraint) Logic Programming is that the algorithmic behavior of (constraint) logic programs and its mathematical interpretations agree with each other, in the sense that the declarative meaning of a program can be interpreted operationally as a goal-oriented search for solutions. Both operational (algorithmic) and declarative (mathematical) semantics for programs are useful and widely developed in the frame of Logic Programming as well as in its extension, Constraint Logic Programming.For these reasons, HH(C) was in need of a more mathematical interpretation of programs. In this paper some fixed point semantics for HH(C) are presented. Taking as a starting point the techniques used by Miller to interpret the fragment of HH that incorporates intuitionistic implication in goals, we have formulated two novel extensions capable of dealing with the whole HH logic, including universal quantifiers, as well as with the presence of constraints. Those semantics are proved to be sound and complete w.r.t. the operational semantics of HH(C).

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Records for logic programming

Cited by 82 publications

References 12 publications

Finite-Tree Analysis for Constraint Logic-Based Languages

Finite-Tree Analysis for Constraint Logic-Based Languages

Logic programming in the context of multiparadigm programming: the Oz experience

Providing declarative semantics for HH extended constraint logic programs

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