Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Reasoning01:30

Reasoning

346
Reasoning is the action of thinking about something in a logical, sensible way. It is integral to problem-solving, decision-making, and critical thinking. Reasoning can be inductive or deductive. Reasoning involves transforming information into conclusions, which is essential for problem-solving, decision-making, and critical thinking.
Inductive reasoning involves deriving generalizations from specific observations. This type of reasoning helps form beliefs about the world. For example,...
346
Deductive Reasoning01:16

Deductive Reasoning

63.6K
Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...
63.6K
Inductive Reasoning00:59

Inductive Reasoning

64.4K
Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
64.4K
Rational Expressions01:28

Rational Expressions

185
Rational expressions are algebraic fractions in which both the numerator and the denominator are polynomials. These expressions follow the arithmetic rules of numerical fractions but require extra care due to the presence of variables. A fundamental part of working with rational expressions is identifying values that make the expression undefined, typically those that result in division by zero or undefined radicals.Determining the DomainThe domain of a rational expression includes all real...
185
Reason and Intuition01:37

Reason and Intuition

7.3K
The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
7.3K
Relation between Mathematical Equations and Block Diagrams01:20

Relation between Mathematical Equations and Block Diagrams

2.7K
In a spring-mass-damper system, the second-order differential equation describes the dynamic behavior of the system. When transformed into the Laplace domain under zero initial conditions, this equation can be effectively analyzed and manipulated. The transformation into the Laplace domain converts differential equations into algebraic equations, simplifying the process of isolating the output.
2.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A biomimetic liquid metal cell.

Nanoscale·2026
Same author

The impact of genotype-phenotype on the prognosis of children with hypertrophic cardiomyopathy.

JTCVS open·2026
Same author

Ca2+ sensor CBL2/3-CIPK3/9/26 phosphorylates MTP11 to modulate manganese efflux via degradation and Golgi retention.

Plant physiology·2026
Same author

Pan-neurodegeneration proteomics reveals disease subtypes and molecular signatures.

Cell·2026
Same author

Associations between metal mixture exposure and emotional distress symptoms among pregnant women: A prospective study.

Journal of trace elements in medicine and biology : organ of the Society for Minerals and Trace Elements (GMS)·2026
Same author

Sox10 glial-neuron nuclear communication mediates anti-apoptosis neuroprotection after ischaemic stroke.

Stroke and vascular neurology·2026
See all related articles

Related Experiment Video

Updated: Dec 24, 2025

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.6K

Reasoning within expressive fuzzy rough description logics.

Yuncheng Jiang1, Ju Wang1, Peimin Deng1

  • 1School of Computer Science and Information Technology, Guangxi Normal University, Guilin 541004, PR China.

Fuzzy Sets and Systems
|April 15, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces fuzzy rough Description Logics (DLs) to manage imprecise knowledge in AI applications. This new framework integrates fuzzy and rough set theories, simplifying complex reasoning tasks in knowledge-based systems.

Keywords:
Description logicsFuzzy description logicsFuzzy rough description logicsFuzzy rough set theoryRough description logics

More Related Videos

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients
05:48

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

Published on: June 12, 2020

6.2K
Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.3K

Related Experiment Videos

Last Updated: Dec 24, 2025

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.6K
The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients
05:48

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

Published on: June 12, 2020

6.2K
Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.3K

Area of Science:

  • Artificial Intelligence
  • Knowledge Representation and Reasoning

Background:

  • Managing imprecision and vagueness is crucial for intelligent knowledge-based applications.
  • Description Logics (DLs) are established formalisms for structured knowledge representation.
  • Existing DLs face challenges in handling uncertain or imprecise information.

Purpose of the Study:

  • To analyze current research and problems in uncertain knowledge representation within DLs.
  • To propose an integration of fuzzy DLs and rough DLs, creating fuzzy rough DLs.
  • To define the syntax, semantics, and properties of this new fuzzy rough DL framework.

Main Methods:

  • The study integrates fuzzy DLs and rough DLs based on fuzzy rough set theory.
  • Formal definitions for the syntax, semantics, and properties of fuzzy rough DLs are provided.
  • Reasoning procedures in fuzzy rough DLs are analyzed and reduced.

Main Results:

  • A novel fuzzy rough DL framework is introduced, combining fuzzy and rough set theories.
  • The formal properties of fuzzy rough DLs are established.
  • Key reasoning tasks in fuzzy rough DLs are shown to be reducible to ABox consistency checking in fuzzy DLs.

Conclusions:

  • Fuzzy rough DLs offer a unified approach to managing vagueness and imprecision in knowledge representation.
  • The proposed framework simplifies reasoning by reducing complex tasks to ABox consistency.
  • This research advances the development of more realistic and intelligent knowledge-based systems.