This paper introduces an evidential reasoning-based approach for recognizing and extracting manufacturing features from solid model description of objects. A major di4culty faced by previously proposed methods for feature extraction has been the interaction between features due to non-uniqueness and ambiguousness in feature representation. To overcome this di4culty, we introduce a Dempster–Shaf...

We present two methods to provide explanations for reasoning with belief functions. One approach, inspired by Strat's method, is based on sensitivity analysis, but its computation is simpler thus easier to implement than Strat's. The other approach is to examine the impact of each piece of evidence on the conclusion based on the measure of the information content provided by the evidence. We sh...

In the existing evidential networks with belief functions, the relations among the variables are always represented by joint belief functions on the product space of the involved variables. In this paper, we use conditional belief functions to represent such relations in the network and show some relations of these two kinds of representations. We also present a propagation algorithm for such n...

In the real world, one usually cannot gather enough evidence to completely determine the activity of a system. Typically, different pieces of evidence tell you about different aspects of the system with different certainties. Correlating these at a given point of time is a much studied problem. The problem becomes even less tractable when the evidence is acquired at different points in time. Th...

Motives play an important role at every stage of a criminal investigation. They can be used to search for an explanation of the crime (why was this person killed?), to identify a suspect (who would have killed this person for this reason), and to persuade a jury of a suspect’s guilt (this motive explains why this person committed this crime). We have previously developed an account of motivatio...

This paper describes our work on an integrated system that can assist analysts in exploring hypotheses using Bayesian analysis of evidence from a variety of sources. The hypothesis exploration is aided by an ontology that represents domain knowledge, events, and causality for Bayesian reasoning, as well as models of information sources for evidential reasoning. We are validating the approach vi...

Several reasoning tasks need to scale over the volume of evidence and the entities of interest. A technique used by statisticians is to represent a collection of evidence by the sufficient statistics of the data. This reduces the computation burden on the BN model significantly by representing a large set of records (and therefore nodes) with a single node. However, this procedure is not genera...

Evidential reasoning is cast as the problem of simplifying the evidence-hypothesis relation and constructing combination formulas that possess certain testable properties. Important classes of evidence as identifiers, annihilators, and idempotents and their roles in determining binary operations on intervals of reals are discussed. The appropriate way of constructing formulas for combining evid...

This study looks at two aspects of assurance services. The first deals with the type(s) of evidential networks that will allow a professional accountant to provide assurance. Here, we develop an evidential network model for “WebTrust Assurance,” a service being provided by the American Institute of Certified Public Accountants (AICPA) and the Canadian Institute of Chartered Accountants (CICA). ...

Article history: Received 26 January 2013 Received in revised form 9 September 2013 Accepted 13 September 2013 Available online 23 September 2013