2008
is a high level knowledge representation framework that allows us to solve problems declaratively based on abductive reasoning. It extends normal Logic Programming by allowing some predicates to be incompletely defined, declared as abducible predicates. Problem solving is effected by deriving hypotheses on these abducible predicates (abductive hypotheses) as solutions of problems to be solved. These problems can be either observations that need to be explained (as in classical abduction) or goals to be achieved (as in normal logic programming). It can be used to solve problems
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Institutional Research Information System (University of Udine) · 2015
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Apt, K.R. (Krzysztof) · CWI's Institutional Repository (Centrum Wiskunde & Informatica) · 1990
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Krzysztof R. Apt · Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands · 1996
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Serebrenik, Alexander, Demoen, Bart · Lecture notes in computer science · 2003
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Reda Alhajj, Jon Rokne · 2018
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Kent D. Lee · Undergraduate topics in computer science · 2017
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Saul I. Gass, Carl M. Harris · 2001
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Agostino Dovier, Andrea Formisano, Alberto Policriti · 2007
T -resolution parametrically generalizes standard resolution with respect to a first-order theory T (the parameter). The inherent power of its derivation rule, however, makes it difficult to develop efficient unrestricted T -resolution based systems. CLP (X ) parametrically extends Horn clause logic programming with respect to a domain of computation X . The theory T underlying the domain X is fixed a-priori and can not be modified (extended) by the user. In this paper we present the parametric logic programming language T logic programming (TLP) which extends the CLP -scheme by giving the possibility of acting on the theory T . The scheme is embedded into (linear) T -resolution; however, its syntax ensures that three rules (simple instances of the general T -resolution rule) are sufficient to implement the derivation process. Keywords: Theorem Proving, Constraint Logic Programming, Foundations. 1 Introduction The main motivation of this work is to establish a link between automat...
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Krzysztof R. Apt · Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands · 1990
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Peter Norvig · Elsevier eBooks · 1992
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SpringerReference · 2011
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Isaac Balbin, Koenraad Lecot · 1985
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Birkhäuser Boston eBooks · 2008
AbstractIn this section we show that the execution of a program can be understood as the automated deduction of the empty clause from a given unsatisfiable clause set (possibly using the resolution refinements from Section 2.6). A further concept is needed: how to generate an answer, a result of the computation, from the resolution proof. A resolution proof as such shows only that the empty clause is derivable; an answer, in a sense, explains how it is obtained. The following ideas of extracting an answer from a resolution proof have their roots in the works of Green and Kowalski.KeywordsLogic ProgramLogic ProgrammingAtomic FormulaHorn ClauseGround InstanceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Michael Gelfond · Wiley Encyclopedia of Electrical and Electronics Engineering · 1999
Abstract The sections in this article are Syntax of Pure Prolog Inference in Pure Prolog Representing Incomplete Information Negation as Failure History
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Martin H. Weik · 2000
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H.C. Doets · UvA-DARE (University of Amsterdam) · 1997
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Robert Kowalski · Handbook of the history of logic · 2014
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Steve Barker, Michael Leuschel, Mauricio Varea · 2008
We describe the use of a flexible meta-interpreter for performing access control checks on deductive databases. The meta-program is implemented in Prolog and takes as input a database and an access policy specification. We then proceed to specialise the meta-program for a given access policy and intensional database by using the logen partial evaluation system. In addition to describing the programs involved in our approach, we give a number of performance measures for our implementation of an access control checker, and we discuss the implications of using this approach for access control on deductive databases. In particular, we show that by using our approach we get flexible access control with virtually zero overhead. Categories and Subject Descriptors H.2.7 [Database Management]: Database Administration—security, integrity, and protection; F.3.2 [Logics and
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Board Of, Comp Sc · 1992
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