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This paper employs the linear nested sequent framework to design a new cut-free calculus LNIF for intuitionistic fuzzy logic--the first-order G\"odel logic characterized by linear relational frames with constant domains. Linear nested…

Logic in Computer Science · Computer Science 2020-10-06 Tim Lyon

We look at the problem of revising fuzzy belief bases, i.e., belief base revision in which both formulas in the base as well as revision-input formulas can come attached with varying truth-degrees. Working within a very general framework…

Artificial Intelligence · Computer Science 2012-12-12 Richard Booth , Eva Richter

In medicine one frequently deals with vague information. As a tool for reasoning in this area, fuzzy logic suggests itself. In this paper we explore the applicability of the basic ideas of fuzzy set theory in the context of medical…

Logic · Mathematics 2018-08-31 Thomas Vetterlein , Anna Zamansky

In this paper we show several similarities among logic systems that deal simultaneously with deductive and quantitative inference. We claim it is appropriate to call the tasks those systems perform as Quantitative Logic Reasoning. Analogous…

Logic in Computer Science · Computer Science 2019-05-15 Marcelo Finger

Nonmonotonic logics are usually characterized by the presence of some notion of 'conditional' that fails monotonicity. Research on nonmonotonic logics is therefore largely concerned with the defeasibility of argument forms and the…

Logic in Computer Science · Computer Science 2013-10-29 Katarina Britz , Ivan Varzinczak

Propositional term modal logic is interpreted over Kripke structures with unboundedly many accessibility relations and hence the syntax admits variables indexing modalities and quantification over them. This logic is undecidable, and we…

Logic in Computer Science · Computer Science 2019-01-01 Anantha Padmanabha , R Ramanujam

In proof-theoretic semantics, meaning is based on inference. It may seen as the mathematical expression of the inferentialist interpretation of logic. Much recent work has focused on base-extension semantics, in which the validity of…

Logic · Mathematics 2024-02-13 Timo Eckhardt , David J. Pym

In a recent paper [1] we introduced the Fuzzy Bayesian Learning (FBL) paradigm where expert opinions can be encoded in the form of fuzzy rule bases and the hyper-parameters of the fuzzy sets can be learned from data using a Bayesian…

Machine Learning · Statistics 2017-04-07 Indranil Pan , Dirk Bester

Collocations are important for many tasks of Natural language processing such as information retrieval, machine translation, computational lexicography etc. So far many statistical methods have been used for collocation extraction. Almost…

Computation and Language · Computer Science 2008-11-11 Raj Kishor Bisht , H. S. Dhami

Logic $L$ was introduced by Lewitzka [7] as a modal system that combines intuitionistic and classical logic: $L$ is a conservative extension of CPC and it contains a copy of IPC via the embedding $\varphi\mapsto\square\varphi$. In this…

Logic in Computer Science · Computer Science 2017-03-10 Steffen Lewitzka

Existential rules form an expressive Datalog-based language to specify ontological knowledge. The presence of existential quantification in rule-heads, however, makes the main reasoning tasks undecidable. To overcome this limitation, in the…

Computational Complexity · Computer Science 2023-07-25 Georg Gottlob , Marco Manna , Cinzia Marte

Limits and colimits of diagrams, defined by maps between sets, are universal constructions fundamental in different mathematical domains and key concepts in theoretical computer science. Its importance in semantic modeling is described by…

Logic in Computer Science · Computer Science 2016-04-12 Carlos Leandro , Luís Monteiro

Fusions are a simple way of combining logics. For normal modal logics, fusions have been investigated in detail. In particular, it is known that, under certain conditions, decidability transfers from the component logics to their fusion.…

Artificial Intelligence · Computer Science 2011-06-10 F. Baader , C. Lutz , H. Sturm , F. Wolter

Fuzzy implication functions are a key area of study in fuzzy logic, extending the classical logical conditional to handle truth degrees in the interval $[0,1]$. While existing literature often focuses on a limited number of families, in the…

Artificial Intelligence · Computer Science 2025-03-11 Raquel Fernandez-Peralta

Recent ideas about epistemic modals and indicative conditionals in formal semantics have significant overlap with ideas in modal logic and dynamic epistemic logic. The purpose of this paper is to show how greater interaction between formal…

Logic in Computer Science · Computer Science 2017-08-07 Wesley H. Holliday , Thomas F. Icard

In \cite{Lyon24} the question of the decidability of quasi-dense modal logics is answered, and an upper bound in $\EXPSPACE$ is given. Unfortunately, authors' intricate proof seems to contain a major flaw that cannot be fixed, leaving the…

Logic in Computer Science · Computer Science 2025-08-11 Olivier Gasquet

Fuzzy quantification is a subtopic of fuzzy logic which deals with the modelling of the quantified expressions we can find in natural language. Fuzzy quantifiers have been successfully applied in several fields like fuzzy, control, fuzzy…

Artificial Intelligence · Computer Science 2018-07-20 F. Díaz-Hermida , Juan. C. Vidal

This paper is a continuation of our work concerning the logical and computational foundations of multi-step fuzzy inference. We bring further results on the implementation of the Mamdani-Assilian type of fuzzy rules and inference in Goedel…

Logic in Computer Science · Computer Science 2023-12-12 Dusan Guller

The decidability of axiomatic extensions of the modal logic K with modal reduction principles, i.e. axioms of the form $\Diamond^{k} p \rightarrow \Diamond^{n} p$, has remained a long-standing open problem. In this paper, we make…

Logic in Computer Science · Computer Science 2024-06-06 Piotr Ostropolski-Nalewaja , Tim S. Lyon

Plausible reasoning concerns situations whose inherent lack of precision is not quantified; that is, there are no degrees or levels of precision, and hence no use of numbers like probabilities. A hopefully comprehensive set of principles…

Artificial Intelligence · Computer Science 2017-04-05 David Billington