Scoping constructs in logic programming: Implementation problems and their solution

Nadathur, Gopalan; Jayaraman, Bharat; Kwon, Keehang

doi:10.1016/0743-1066(95)00037-k

Cited by 16 publications

(15 citation statements)

References 10 publications

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“…In another direction, it includes a polymorphic typing regimen (Nadathur and Pfenning 1992). Both aspects raise new questions for implementation that we have addressed elsewhere (Kwon et al 1994;Nadathur et al 1995). The machinery that we describe here blends well with these other mechanisms and all our ideas have, in fact, been amalgamated in a new implementation of λProlog called Teyjus (Nadathur and Mitchell 1999).…”

Section: Introductionmentioning

confidence: 92%

A treatment of higher-order features in logic programming

Nadathur

2005

Theory and Practice of Logic Programming

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The logic programming paradigm provides the basis for a new intensional view of higherorder notions. This view is realized primarily by employing the terms of a typed lambda calculus as representational devices and by using a richer form of unification for probing their structures. These additions have important meta-programming applications but they also pose non-trivial implementation problems. One issue concerns the machine representation of lambda terms suitable to their intended use: an adequate encoding must facilitate comparison operations over terms in addition to supporting the usual reduction computation. Another aspect relates to the treatment of a unification operation that has a branching character and that sometimes calls for the delaying of the solution of unification problems. A final issue concerns the execution of goals whose structures become apparent only in the course of computation. These various problems are exposed in this paper and solutions to them are described. A satisfactory representation for lambda terms is developed by exploiting the nameless notation of de Bruijn as well as explicit encodings of substitutions. Special mechanisms are molded into the structure of traditional Prolog implementations to support branching in unification and carrying of unification problems over other computation steps; a premium is placed in this context on exploiting determinism and on emulating usual first-order behaviour. An extended compilation model is presented that treats higher-order unification and also handles dynamically emergent goals. The ideas described here have been employed in the Teyjus implementation of the λProlog language, a fact that is used to obtain a preliminary assessment of their efficacy.

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Section: Introductionmentioning

confidence: 92%

A treatment of higher-order features in logic programming

Nadathur

2005

Theory and Practice of Logic Programming

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“…A detailed discussion of how a solver for such formulas works is not relevant for this paper. The basic structure of the solver is that of a standard resolutionbased Prolog solver, and the additional features (quantification, implications as goals) are handled as described in [32].…”

Section: The F P + Module Description Logicmentioning

confidence: 99%

“…Formally, the set ofF P + formulas is a subset of the firstorder hereditary Harrop formulas (fohh) [32], and the proof method (which was sketched in the motivation in Sec. 3) for fohh agrees with the classical proof-theoretical semantics in predicate logic [32]. A detailed discussion of how a solver for such formulas works is not relevant for this paper.…”

Section: The F P + Module Description Logicmentioning

confidence: 99%

“…The logic we use in our examples, F P + , consists of the "pure" and monotonic core of Prolog [37] (which means: no negation-as-failure or other closed-world reasoning, no cuts, no side effects), and a few simple extensions well-known from λProlog [32,33] and its extensions. Hence we do not claim any originality of our solver from the perspective of logic programming and theorem proving.…”

Section: The F P + Module Description Logicmentioning

confidence: 99%

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Modular logic metaprogramming

KloseKarl¹,

OstermannKlaus²

2010

SIGPLAN Not.

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In logic metaprogramming, programs are not stored as plain textfiles but rather derived from a deductive database. While the benefits of this approach for metaprogramming are obvious, its incompatibility with separate checking limits its applicability to large-scale projects. We analyze the problems inhibiting separate checking and propose a class of logics that reconcile logic metaprogramming and separate checking. We have formalized the resulting module system and have proven the soundness of separate checking. We validate its feasibility by presenting the design and implementation of a specific logic that is able to express many metaprogramming examples from the literature.

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“…See [5] for a functional programming implementations of interpreters for languages such as LA and XProlog; see [23,261 for discussions concerning the compilation of these languages.…”

Section: Examples Of La Programsmentioning

confidence: 99%

A logic programming language with lambda-abstraction, function variables, and simple unification

Miller

Extensions of Logic Programming

133

125

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It has been argued elsewhere that a logic programming language with function variables and λ-abstractions within terms makes a good meta-programming language, especially when an object-language contains notions of bound variables and scope. The λProlog logic programming language and the related Elf and Isabelle systems provide meta-programs with both function .variables and λ-abstractions by containing implementations of higher-order unification. This paper presents a logic programming language, called Lλ, that also contains both function variables and λ-abstractions, although certain restrictions are placed on occurrences of function variables. As a result of these restrictions, an implementation of Lλ does not need to implement full higher order unification. Instead, an extension to first-order unification that respects bound variable names and scopes is all that is required. Such unification problems are shown to be decidable and to possess most general unifiers when unifiers exist. A unification algorithm and logic programming interpreter are described and proved correct. Several examples of using Lλ as a meta-programming language are presented. Abstract: It has been argued elsewhere that a logic programming language with function variables and A-abstractions within terms makes a good meta-programming language, especially when an object-language contains notions of bound variables and scope. The AProlog logic programming language and the related Elf and Isabelle systems provide meta-programs with both function .variables and A-abstractions by containing implementations of higher-order unification. This paper presents a logic programming language, called LA, that also contains both function variables and A-abstractions, although certain restrictions are placed on occurrences of function variables. As a result of these restrictions, an implementation of LA does not need to implement full higherorder unification. Instead, an extension to first-order unification that respects bound variable names and scopes is all that is required. Such unification problems are shown to be decidable and to possess most general unifiers when unifiers exist. A unification algorithm and logic programming interpreter are described and proved correct. Several examples of using LA as a meta-programming language are presented.

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Scoping constructs in logic programming: Implementation problems and their solution

Cited by 16 publications

References 10 publications

A treatment of higher-order features in logic programming

A treatment of higher-order features in logic programming

Modular logic metaprogramming

A logic programming language with lambda-abstraction, function variables, and simple unification

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