Published on Wed May 12 2021

MMGET: A Markov model for generalized evidence theory

Yuanpeng He
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Abstract

In real life, lots of information merges from time to time. To appropriately describe the actual situations, lots of theories have been proposed. Among them, Dempster-Shafer evidence theory is a very useful tool in managing uncertain information. To better adapt to complex situations of open world, a generalized evidence theory is designed. However, everything occurs in sequence and owns some underlying relationships with each other. In order to further embody the details of information and better conforms to situations of real world, a Markov model is introduced into the generalized evidence theory which helps extract complete information volume from evidence provided. Besides, some numerical examples is offered to verify the correctness and rationality of the proposed method.

Thu Apr 17 2014
Artificial Intelligence
Generalized Evidence Theory
Conflict management is still an open issue in the application of Dempster Shafer evidence theory. In this paper, a new theory, called as generalized evidence theory(GET), is proposed. GET assumes that the general situation is in open world due to the uncertainty and incomplete knowledge.
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Fri Nov 09 2018
Artificial Intelligence
Mathematical Theory of Evidence Versus Evidence
This paper is concerned with the apparent greatest weakness of the Mathematical Theory of Evidence (MTE) of Shafer. It is shown that shaferian conditioning of belief functions on observations may be treated as selection combined with modification.
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Fri Nov 09 2018
Artificial Intelligence
Reasoning From Data in the Mathematical Theory of Evidence
Mathematical Theory of Evidence (MTE) is known as a foundation for reasoning. It is claimed in this paper that MTE is suitable to model some types of destructive processes.
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Sat Nov 16 2013
Artificial Intelligence
A generalized evidence distance
Dempster-Shafer theory of evidence (D-S theory) is widely used in uncertain information process. How to measure the distance between two BPAs is an open issue. The proposed method is a generalized of existing evidence distance.
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Tue Feb 24 2015
Artificial Intelligence
Transformation of basic probability assignments to probabilities based on a new entropy measure
Dempster-Shafer evidence theory is an efficient mathematical tool to deal with uncertain information. In that theory, basic probability assignment (BPA) is the basic element for the expression and inference of uncertainty. A novel approach of transforming basic probability assignments to probabilities is proposed.
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Wed May 10 2017
Artificial Intelligence
An evidential Markov decision making model
The sure thing principle and the law of total probability are basic laws in classic probability theory. A disjunction fallacy leads to the violation of these two classical laws. In this paper, an Evidential Markov (EM) decision making model is proposed to address this issue.
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