scholarly journals Correctness criteria of some algorithms for uncertain reasoning using Incidence Calculus

1986 ◽  
Vol 2 (2) ◽  
pp. 109-126 ◽  
Author(s):  
Alan Bundy
Keyword(s):  
Uncertain Reasoning ◽  
Incidence Calculus

scholarly journals Uncertain reasoning

2021 ◽  
Author(s):  
Alessandro Antonucci ◽  
Salem Benferhat ◽  
Kamal Premaratne
Keyword(s):  
Uncertain Reasoning

Bayesian Networks Application in Multi-State System Reliability Analysis

2013 ◽  
Vol 347-350 ◽  
pp. 2590-2595 ◽  
Author(s):  
Sheng Zhai ◽  
Shu Zhong Lin
Keyword(s):  
Bayesian Network ◽  
Reliability Analysis ◽  
System Reliability ◽  
Prior Probability ◽  
State System ◽  
Weak Links ◽  
Uncertain Reasoning ◽  
Reliability Modeling ◽  
Line System ◽  
Reliability Indices

Aiming at the limitations of traditional reliability analysis theory in multi-state system, a method for reliability modeling and assessment of a multi-state system based on Bayesian Network (BN) is proposed with the advantages of uncertain reasoning and describing multi-state of event. Through the case of cell production line system, in this paper we will discuss how to establish and construct a multi-state system model based on Bayesian network, and how to apply the prior probability and posterior probability to do the bidirectional inference analysis, and directly calculate the reliability indices of the system by means of prior probability and Conditional Probability Table (CPT) . Thereby we can do the qualitative and quantitative analysis of the multi-state system reliability, identify the weak links of the system, and achieve assessment of system reliability.

scholarly journals Assignment methods for incidence calculus

1995 ◽  
Vol 12 (1) ◽  
pp. 21-41 ◽  
Author(s):  
R.G. McLean ◽  
A. Bundy ◽  
W. Liu
Keyword(s):  
Incidence Calculus

Predicting Corporate Acquisitions: An Application of Uncertain Reasoning Using Rule Induction

2003 ◽  
Vol 5 (4) ◽  
pp. 401-412 ◽  
Author(s):  
Srinivasan Ragothaman ◽  
Bijayananda Naik ◽  
Kumoli Ramakrishnan
Keyword(s):  
Rule Induction ◽  
Uncertain Reasoning ◽  
Corporate Acquisitions

scholarly journals Application of Uncertain Reasoning to Business Decisions: An Introduction

2003 ◽  
Vol 5 (4) ◽  
pp. 343-344 ◽  
Author(s):  
Prakash P. Shenoy ◽  
Rajendra P. Srivastava
Keyword(s):  
Uncertain Reasoning ◽  
Business Decisions

scholarly journals Argumentation as a General Framework for Uncertain Reasoning

1993 ◽  
pp. 428-434 ◽  
Author(s):  
John Fox ◽  
Paul Krause ◽  
Morten EIvang-Gøransson
Keyword(s):  
General Framework ◽  
Uncertain Reasoning

scholarly journals Qualitative and Quantitative Integrated Modeling for Stochastic Simulation and Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Xuefeng Yan ◽  
Yong Zhou ◽  
Yan Wen ◽  
Xudong Chai
Keyword(s):  
Hierarchical Model ◽  
Description Logic ◽  
Expert Knowledge ◽  
Uncertain Reasoning ◽  
Model Structure ◽  
Meta Model ◽  
Simulation And Optimization ◽  
Qualitative And Quantitative ◽  
Level Model ◽  
High Level

The simulation and optimization of an actual physics system are usually constructed based on the stochastic models, which have both qualitative and quantitative characteristics inherently. Most modeling specifications and frameworks find it difficult to describe the qualitative model directly. In order to deal with the expert knowledge, uncertain reasoning, and other qualitative information, a qualitative and quantitative combined modeling specification was proposed based on a hierarchical model structure framework. The new modeling approach is based on a hierarchical model structure which includes the meta-meta model, the meta-model and the high-level model. A description logic system is defined for formal definition and verification of the new modeling specification. A stochastic defense simulation was developed to illustrate how to model the system and optimize the result. The result shows that the proposed method can describe the complex system more comprehensively, and the survival probability of the target is higher by introducing qualitative models into quantitative simulation.

Intelligent Classification and Ranking Analyses Using CARBS

2009 ◽  
pp. 236-253
Author(s):  
Malcolm J. Beynon
Keyword(s):  
Data Analysis ◽  
Uncertain Reasoning ◽  
Intelligent Data Analysis ◽  
Dempster Shafer Theory ◽  
Financial Strength ◽  
Strength Rating ◽  
Shafer Theory

This chapter demonstrates intelligent data analysis, within the environment of uncertain reasoning, using the recently introduced CaRBS technique that has its mathematical rudiments in Dempster-Shafer theory. A series of classification and ranking analyses are undertaken on a bank rating application, looking at Moody’s bank financial strength rating (BFSR). The results presented involve the association of each bank to being low or high BFSR, with emphasis is on the graphical exposition of the results including the use of a series of simplex plots. Throughout the analysis there is discussion on how the present of ignorance in the results should be handled, whether it should be excluded (belief) or included (plausibility) in the evidence supporting the classification or ranking of the banks.

Effective Intelligent Data Mining Using Dempster-Shafer Theory

2008 ◽  
pp. 2943-2963
Author(s):  
Malcolm J. Beynon
Keyword(s):  
Data Mining ◽  
Missing Values ◽  
Uncertain Reasoning ◽  
Worst Case ◽  
Data Mining Techniques ◽  
Imperfect Data ◽  
Dempster Shafer Theory ◽  
External Management ◽  
Shafer Theory

The efficacy of data mining lies in its ability to identify relationships amongst data. This chapter investigates that constraining this efficacy is the quality of the data analysed, including whether the data is imprecise or in the worst case incomplete. Through the description of Dempster-Shafer theory (DST), a general methodology based on uncertain reasoning, it argues that traditional data mining techniques are not structured to handle such imperfect data, instead requiring the external management of missing values, and so forth. One DST based technique is classification and ranking belief simplex (CaRBS), which allows intelligent data mining through the acceptance of missing values in the data analysed, considering them a factor of ignorance, and not requiring their external management. Results presented here, using CaRBS and a number of simplex plots, show the effect of managing and not managing of imperfect data.

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