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Argumentation frameworks, consisting of arguments and an attack relation representing conflicts, are fundamental for formally studying reasoning under conflicting information. We use methods from mathematical logic, specifically…

Artificial Intelligence · Computer Science 2025-12-01 Uri Andrews , Luca San Mauro

We discuss proving correctness and completeness of definite clause logic programs. We propose a method for proving completeness, while for proving correctness we employ a method which should be well known but is often neglected. Also, we…

Logic in Computer Science · Computer Science 2017-01-31 Włodzimierz Drabent

Argumentation is one of the most popular approaches of defining a~non-monotonic formalism and several argumentation based semantics were proposed for defeasible logic programs. Recently, a new approach based on notions of conflict…

Artificial Intelligence · Computer Science 2014-04-29 Jozef Frtús

We unify functional and logic programming by treating predicatesas functions equipped with their support: the set of inputs whose output is nonzero. Datalog, for instance, is a language of finitely supported boolean functions. Finite…

Programming Languages · Computer Science 2026-04-30 Michael Arntzenius , Max Willsey

Currently, the variety of expressive extensions and different semantics created for logic programs with negation is diverse and heterogeneous, and there is a lack of comprehensive comparative studies which map out the multitude of…

Artificial Intelligence · Computer Science 2009-09-29 Pascal Hitzler , Sibylle Schwarz

We propose a set of transformation rules for constraint logic programs with negation. We assume that every program is locally stratified and, thus, it has a unique perfect model. We give sufficient conditions which ensure that the proposed…

Programming Languages · Computer Science 2007-05-23 Fabio Fioravanti , Alberto Pettorossi , Maurizio Proietti

Program Synthesis is the mapping of a specification of what a computer program is supposed to do, into a computer program that does what the specification says to do. This is equivalent to constructing any computer program and a sound proof…

Logic in Computer Science · Computer Science 2015-01-08 Charles Volkstorf

Higher-order constructs extend the expressiveness of first-order (Constraint) Logic Programming ((C)LP) both syntactically and semantically. At the same time assertions have been in use for some time in (C)LP systems helping programmers…

Programming Languages · Computer Science 2014-06-03 Nataliia Stulova , José F. Morales , Manuel V. Hermenegildo

A logic program is an executable specification. For example, merge sort in pure Prolog is a logical formula, yet shows creditable performance on long linked lists. But such executable specifications are a compromise: the logic is distorted…

Programming Languages · Computer Science 2015-09-29 M. H. van Emden

In many situations humans have to reason with inconsistent knowledge. These inconsistencies may occur due to not fully reliable sources of information. In order to reason with inconsistent knowledge, it is not possible to view a set of…

Artificial Intelligence · Computer Science 2024-12-16 Nico Roos

In David Schmidt's PhD work he explored the use of denotational semantics as a programming language. It was part of an effort to not only treat formal semantics as specifications but also as interpreters and input to compiler generators.…

Programming Languages · Computer Science 2013-09-23 Mads Rosendahl

Incomplete information is a problem in many aspects of actual environments. Furthermore, in many sceneries the knowledge is not represented in a crisp way. It is common to find fuzzy concepts or problems with some level of uncertainty.…

Programming Languages · Computer Science 2007-05-23 Susana Munoz-Hernandez , Claudio Vaucheret

Logic programs P and Q are strongly equivalent if, given any program R, programs P union R and Q union R are equivalent (that is, have the same answer sets). Strong equivalence is convenient for the study of equivalent transformations of…

Logic in Computer Science · Computer Science 2007-05-23 Hudson Turner

We define and study logics in the framework of probabilistic team semantics and over metafinite structures. Our work is paralleled by the recent development of novel axiomatizable and tractable logics in team semantics that are closed under…

Logic in Computer Science · Computer Science 2021-04-12 Miika Hannula , Minna Hirvonen , Juha Kontinen

This paper discusses proof-theoretic semantics, the project of specifying the meanings of the logical constants in terms of rules of inference governing them. I concentrate on Michael Dummett's and Dag Prawitz' philosophical motivations and…

Logic in Computer Science · Computer Science 2021-08-12 Nils Kürbis

Most fuzzy systems including fuzzy decision support and fuzzy control systems provide out-puts in the form of fuzzy sets that represent the inferred conclusions. Linguistic interpretation of such outputs often involves the use of linguistic…

Artificial Intelligence · Computer Science 2013-01-30 Ryszard Kowalczyk

Answer-set programming (ASP) paradigm is a way of using logic to solve search problems. Given a search problem, to solve it one designs a theory in the logic so that models of this theory represent problem solutions. To compute a solution…

Logic in Computer Science · Computer Science 2007-05-23 Deborah East , Miroslaw Truszczynski

In this paper we deal with a new approach to probabilistic reasoning in a logical framework. Nearly almost all logics of probability that have been proposed in the literature are based on classical two-valued logic. After making clear the…

Artificial Intelligence · Computer Science 2013-02-21 Petr Hajek , Lluis Godo , Francesc Esteva

We present a novel approach to construction of a formal semantics for a programming language. Our approach, using a parametric denotational semantics, allows the semantics to be easily extended to support new language features, and…

Programming Languages · Computer Science 2018-12-04 In-Ho Yi

Pattern recognition systems implemented using deep neural networks achieve better results than linear models. However, their drawback is the black box property. This property means that one with no experience utilising nonlinear systems may…

Machine Learning · Computer Science 2024-01-29 Martin Klimo , Lubomir Kralik
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