Authors:
Rajendra P. Srivastava
1
and
Kenneth O. Cogger
2
Affiliations:
1
The University of Kansas, United States
;
2
Peak Consulting, United States
Keyword(s):
Approximate Reasoning, Belief Functions, Uncertainty, Evidential Reasoning, Dempster-Shafer Theory, Joint Distribution of Beliefs.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Knowledge Representation and Reasoning
;
Symbolic Systems
;
Uncertainty in AI
Abstract:
It is quite common in real world situations to form beliefs under Dempster-Shafer (DS) theory on various variables from a single source. This is true, in particular, in auditing. Also, the judgment about these beliefs is easily made in terms of simple support functions on individual variables. However, for propagating beliefs in a network of variables, one needs to convert these beliefs on individual variables to beliefs on the joint space of the variables pertaining to the single source of evidence. Although there are many possible solutions to the above problem that will yield beliefs on the joint space with the desired marginal beliefs, there is no method that will guarantee that the beliefs are derived from the same source, fully dependent evidence. In this article, we describe such a procedure based on a maximal order decomposition algorithm. The procedure is computationally efficient and is supported by objective chi-square and entropy criteria. While such assignments are not
unique, alternative procedures that have been suggested, such as linear programming, are more computationally intensive and result in similar m-value determinations. It should be noted that our maximal order decomposition (i.e., minimum entropy) approach provides m-values on the joint space for fully dependent items of evidence.
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