A pragmatic reconstruction of λProlog

Belleannée, Catherine; Brisset, Pascal; Ridoux, Olivier

doi:10.1016/s0743-1066(98)10038-9

Cited by 7 publications

(4 citation statements)

References 32 publications

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“…It has been implemented in λProlog (Miller and Nadathur, 1986;Belleannee, Brisset, and Ridoux, 1999) as a generic system in which a theorem-prover and a syntax analyzer can be plugged-in for every logic used in descriptions. It is not meant to be efficient, though it can handle several thousand entries.…”

Section: Methodsmentioning

confidence: 99%

Introduction to logical information systems

Ferré

Ridoux²

2004

Information Processing & Management

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Logical Information Systems (LIS) use logic in a uniform way to describe their contents, to query it, to navigate through it, to analyze it, and to maintain it. They can be given an abstract specification that does not depend on the choice of a particular logic, and concrete instances can be obtained by instantiating this specification with a particular logic. In fact, a logic plays in a LIS the role of a schema in data-bases. We present the principles of logical information systems, the constraints they impose on the expression of logics, and hints for their effective implementation.

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Section: Methodsmentioning

confidence: 99%

Introduction to logical information systems

Ferré

Ridoux²

2004

Information Processing & Management

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“…We already know from [5] that λProlog is the minimal extension of Prolog that allows to implement inductive predicates over syntax containing binders. Does it work when applied to data that is meant to contain existentially quantified metavariables too?…”

Section: λProlog Meets Partial Termsmentioning

confidence: 99%

“…First, in ELPI all types and type and sort declarations can be omitted, whereas they are mandatory in Teyjus. Originally, and according to [5], types were necessary to implement Huet's algorithm for higher order unification. However, for several years now the unification algorithm of Teyjus only solves equations in the pattern fragment L λ discovered by Miller [24], which admits most general unifiers and reduce unification to a decidable problem.…”

Section: Rule (G ?-Conv (?? F [Nat_of_ord X]) T)mentioning

confidence: 99%

Implementing type theory in higher order constraint logic programming

Guidi¹,

Coen²,

Tassi³

2019

Math. Struct. Comp. Sci.

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In this paper we are interested in high-level programming languages to implement the core components of an interactive theorem prover for a dependently typed language: the kernel -responsible for type-checking closed terms -and the elaborator -that manipulates terms with holes or, equivalently, partial proof terms.In the first part of the paper we confirm that λProlog, the language developed by Miller and Nadathur since the 80s, is extremely suitable for implementing the kernel, even when efficient techniques like reduction machines are employed.In the second part of the paper we turn our attention to the elaborator and we observe that the eager generative semantics inherited by Prolog makes it impossible to reason by induction over terms containing metavariables. We also conclude that the minimal extension to λProlog that allows to do so is the possibility to delay inductive predicates over flexible terms, turning them into (set of) constraints to be propagated according to user provided constraint propagation rules.Therefore we propose extensions to λProlog to declare and manipulate higher order constraints, and we implement the proposed extensions in the ELPI system. Our test case is the implementation of an elaborator for a type theory as a CLP extension to a kernel written in plain λProlog.

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“…λProlog, see for example [2], is a logic programming language with built-in support for λ-terms and consequently can be used as an implementation language for theorem provers in much the same way as is done in Qu-Prolog. λProlog does not, however, appear to provide as much support as Qu-Prolog does for implementing interactive theorem provers, nor does it appear to have support for multiple threads or even high-level communication bewteen different λProlog processes.…”

Section: Related Workmentioning

confidence: 99%

Agents as Multi-threaded Logical Objects

Clark

Robinson

2002

Computational Logic: Logic Programming and Beyond

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Abstract. In this paper we describe a distributed object oriented logic programming language in which an object is a collection of threads deductively accessing and updating a shared logic program. The key features of the language, such as static and dynamic object methods and multiple inheritance, are illustrated through a series of small examples. We show how we can implement object servers, allowing remote spawning of objects, which we can use as staging posts for mobile agents. We give as an example an information gathering mobile agent that can be queried about the information it has so far gathered whilst it is gathering new information. Finally we define a class of co-operative reasoning agents that can do resource bounded inference for full first order predicate logic, handling multiple queries and information updates concurrently. We believe that the combination of the concurrent OO and the LP programming paradigms produces a powerful tool for quickly implementing rational multi-agent applications on the internet.

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A pragmatic reconstruction of λProlog

Cited by 7 publications

References 32 publications

Introduction to logical information systems

Introduction to logical information systems

Implementing type theory in higher order constraint logic programming

Agents as Multi-threaded Logical Objects

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