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Developing a general information processing model in uncertain environments is fundamental for the advancement of explainable artificial intelligence. Dempster-Shafer theory of evidence is a well-known and effective reasoning method for…

Artificial Intelligence · Computer Science 2024-09-02 Qianli Zhou , Tianxiang Zhan , Yong Deng

The main goal of this paper is to describe an axiomatic utility theory for Dempster-Shafer belief function lotteries. The axiomatic framework used is analogous to von Neumann-Morgenstern's utility theory for probabilistic lotteries as…

Artificial Intelligence · Computer Science 2020-07-16 Thierry Denoeux , Prakash P. Shenoy

How to manage conflict is still an open issue in Dempster-Shafer evidence theory. The correlation coefficient can be used to measure the similarity of evidence in Dempster-Shafer evidence theory. However, existing correlation coefficients…

Artificial Intelligence · Computer Science 2017-02-03 Wen Jiang

Rule based classifiers that use the presence and absence of key sub-strings to make classification decisions have a natural mechanism for quantifying the uncertainty of their precision. For a binary classifier, the key insight is to treat…

Machine Learning · Computer Science 2020-05-20 James Nutaro , Ozgur Ozmen

An evidential reasoning mechanism based on the Dempster-Shafer theory of evidence is introduced. Its performance in real-world image analysis is compared with other mechanisms based on the Bayesian formalism and a simple weight combination…

Computer Vision and Pattern Recognition · Computer Science 2013-04-11 Ze-Nian Li

This paper presents an efficient adaptation and application of the Dempster-Shafer theory of evidence, one that can be used effectively in a massively parallel hierarchical system for visual pattern perception. It describes the techniques…

Artificial Intelligence · Computer Science 2013-04-12 Ze-Nian Li , Leonard Uhr

Multi-sensor data fusion technology plays an important role in real applications. Because of the flexibility and effectiveness in modelling and processing the uncertain information regardless of prior probabilities, Dempster-Shafer evidence…

Artificial Intelligence · Computer Science 2018-06-06 Fuyuan Xiao

In this paper, we generalize the belief function on complex plane from another point of view. We first propose a new concept of complex mass function based on the complex number, called complex basic belief assignment, which is a…

Artificial Intelligence · Computer Science 2019-07-11 Fuyuan Xiao

In this paper, we demonstrate that a new measure of evidence we developed called the Dempster-Shafer p-value which allow for insights and interpretations which retain most of the structure of the p-value while covering for some of the…

Methodology · Statistics 2024-02-28 Kentaro Hoffman , Kai Zhang , Tyler McCormick , Jan Hannig

In the current versions of the Dempster-Shafer theory, the only essential restriction on the validity of the rule of combination is that the sources of evidence must be statistically independent. Under this assumption, it is permissible to…

Artificial Intelligence · Computer Science 2013-04-12 Lotfi Zadeh , Anca Ralescu

Probability intervals are an attractive tool for reasoning under uncertainty. Unlike belief functions, though, they lack a natural probability transformation to be used for decision making in a utility theory framework. In this paper we…

Artificial Intelligence · Computer Science 2022-01-06 Fabio Cuzzolin

A new entropy-like measure as well as a new measure of total uncertainty pertaining to the Dempster-Shafer theory are introduced. It is argued that these measures are better justified than any of the previously proposed candidates.

Artificial Intelligence · Computer Science 2013-03-25 George J. Klir , Behzad Parviz

A common approach to aggregate classification estimates in an ensemble of decision trees is to either use voting or to average the probabilities for each class. The latter takes uncertainty into account, but not the reliability of the…

Machine Learning · Computer Science 2022-08-17 Florian Busch , Moritz Kulessa , Eneldo Loza Mencía , Hendrik Blockeel

Several different uncertain inference systems (UISs) have been developed for representing uncertainty in rule-based expert systems. Some of these, such as Mycin's Certainty Factors, Prospector, and Bayes' Networks were designed as…

Artificial Intelligence · Computer Science 2013-04-15 Ben P. Wise , Max Henrion

This article deals with plausible reasoning from incomplete knowledge about large-scale spatial properties. The availableinformation, consisting of a set of pointwise observations,is extrapolated to neighbour points. We make use of belief…

Artificial Intelligence · Computer Science 2013-01-14 Jerome Lang , Philippe Muller

It is explored that available credible evidence fusion schemes suffer from the potential inconsistency because credibility calculation and Dempster's combination rule-based fusion are sequentially performed in an open-loop style. This paper…

Artificial Intelligence · Computer Science 2025-04-08 Chaoxiong Ma , Yan Liang , Huixia Zhang , Hao Sun

The issue of confidence factors in Knowledge Based Systems has become increasingly important and Dempster-Shafer (DS) theory has become increasingly popular as a basis for these factors. This paper discusses the need for an empirical…

Artificial Intelligence · Computer Science 2013-04-15 John F. Lemmer

Dempster/Shafer (D/S) theory has been advocated as a way of representing incompleteness of evidence in a system's knowledge base. Methods now exist for propagating beliefs through chains of inference. This paper discusses how rules with…

Artificial Intelligence · Computer Science 2013-04-10 Paul K. Black , Kathryn Blackmond Laskey

The process of information fusion needs to deal with a large number of uncertain information with multi-source, heterogeneity, inaccuracy, unreliability, and incompleteness. In practical engineering applications, Dempster-Shafer evidence…

Artificial Intelligence · Computer Science 2020-04-07 Dongdong Wu , Zijing Liu , Yongchuan Tang

This essay looks at decision-making with interval-valued probability measures. Existing decision methods have either supplemented expected utility methods with additional criteria of optimality, or have attempted to supplement the…

Artificial Intelligence · Computer Science 2013-04-15 Ronald P. Loui