OWL and Qualitative Reasoning Models

2007 ◽  
pp. 33-48 ◽  
Author(s):  
Jochem Liem ◽  
Bert Bredeweg
Keyword(s):  
Qualitative Reasoning

Qualitative Reasoning: A Survey of Techniques Applications

1995 ◽  
Vol 8 (3-4) ◽  
pp. 119-192 ◽  
Author(s):  
◽  
Philippe Dague
Keyword(s):  
Qualitative Reasoning

Systems thinking, a consilience of values and logic

2005 ◽  
Vol 24 (4) ◽  
pp. 259-274
Author(s):  
Sameer Kumar ◽  
Thomas Ressler ◽  
Mark Ahrens
Keyword(s):  
Decision Making ◽  
Problem Solving ◽  
Systems Thinking ◽  
Qualitative Reasoning ◽  
Colleges And Universities ◽  
Decision Makers ◽  
Modern World ◽  
Problem Solving Process ◽  
Problem Solvers

This article is an appeal to incorporate qualitative reasoning into quantitative topics and courses, especially those devoted to decision-making offered in colleges and universities. Students, many of whom join professional workforce, must become more systems thinkers and decision-makers than merely problem-solvers. This will entail discussion of systems thinking, not just reaching “the answer”. Managers will need to formally and forcefully discuss objectives and values at each stage of the problem-solving process – at the start, during the problem-solving stage, and at the interpretation of the results stage – in order to move from problem solving to decision-making. The authors suggest some methods for doing this, and provide examples of why doing so is so important for decision-makers in the modern world.

A qualitative reasoning model of algal bloom in the Danube Delta Biosphere Reserve (DDBR)

2009 ◽  
Vol 4 (5-6) ◽  
pp. 282-298 ◽  
Author(s):  
E. Cioaca ◽  
F.E. Linnebank ◽  
B. Bredeweg ◽  
P. Salles
Keyword(s):  
Algal Bloom ◽  
Biosphere Reserve ◽  
Qualitative Reasoning ◽  
Danube Delta ◽  
Danube Delta Biosphere Reserve

FDI approach to the DAMADICS benchmark problem based on qualitative reasoning coupled with fuzzy neural networks

2006 ◽  
Vol 14 (6) ◽  
pp. 685-698 ◽  
Author(s):  
J.M.F. Calado ◽  
J.M.G. Sá da Costa ◽  
M. Bartys ◽  
J. Korbicz
Keyword(s):  
Neural Networks ◽  
Qualitative Reasoning ◽  
Fuzzy Neural Networks ◽  
Benchmark Problem ◽  
Fuzzy Neural

A Framework for Building Behavioral Models for Design-Stage Failure Identification Using Dimensional Analysis

2010 ◽  
Author(s):  
Eric Coatane´a ◽  
Tuomas Ritola ◽  
Irem Y. Tumer ◽  
David Jensen
Keyword(s):  
Dimensional Analysis ◽  
Behavioral Model ◽  
Qualitative Reasoning ◽  
Design Stage ◽  
Critical Event ◽  
Behavioral Models ◽  
New Approach ◽  
Design Variables ◽  
Qualitative Physics ◽  
Failure Identification

In this paper, a design-stage failure identification framework is proposed using a modeling and simulation approach based on Dimensional Analysis and qualitative physics. The proposed framework is intended to provide a new approach to model the behavior in the Functional-Failure Identification and Propagation (FFIP) framework, which estimates potential faults and their propagation paths under critical event scenarios. The initial FFIP framework is based on combining hierarchical system models of functionality and configuration, with behavioral simulation and qualitative reasoning. This paper proposes to develop a behavioral model derived from information available at the configuration level. Specifically, the new behavioral model uses design variables, which are associated with units and quantities (i.e., Mass, Length, Time, etc…). The proposed framework continues the work to allow the analysis of functional failures and fault propagation at a highly abstract system concept level before any potentially high-cost design commitments are made. The main contribution in this paper consists of developing component behavioral models based on the combination of fundamental design variables used to describe components and their units or quantities, more precisely describing components’ behavior.

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