The Dempster–Shafer (DS) theory of probabilistic reasoning is presented in terms of a semantics whereby every meaningful formal assertion is associated with a triple (p,q, r) where p is the probability ‘‘for’’ the assertion, q is the probability ‘‘against’’ the assertion, and r is the probability of ‘‘don’t know’’. Arguments are presented for the necessity of ‘‘don’t know’’. Elements of the cal...

We develop a new approach for decision making with Dempster-Shafer theory of evidence by using uncertain information represented in the form of interval numbers. We suggest the use of different types of uncertain aggregation operators in the problem. Then, we obtain new aggregation operators such as the belief structure uncertain ordered weighted averaging (BSUOWA) operator and the BS uncertain...

Over the years, there have been many proposed methods in set-based tracking. One example of set-based methods is the use of Dempster-Shafer (DS) techniques to support belief-function (BF) tracking. In this paper, we overview the issues and concepts that motivated DS methods for simultaneous tracking and classification/identification. DS methods have some attributes, if applied correctly; but th...

This paper presents an original method for evaluating reliability indices for Multi-State Systems (MSSs) in the presence of aleatory and epistemic uncertainties. In many real world MSSs an insufficiency of data makes it difficult to estimate precise values for component state probabilities. The proposed approach applies the Transferable Belief Model (TBM) interpretation of the Dempster-Shafer t...

Intelligent help systems aim at providing optimal help to the users of complex application systems. In this context plan recognition is essential for a cooperative system behaviour in that it allows the prediction of future user actions, the ascertainment of suboptimal action sequences or even serves as a basis for user-adapted tutoring or learning components. In this paper a new approach to in...

Suitability of a diagnosis support and its intuitive transparency depend on medical knowledge representation. An appropriate representation should solve the following problems: a consistent interpretation of symptoms of different nature, formulation of rules that is close to diagnostic heuristics and implementation of a credibility measure that estimates diagnostic hypotheses. The paper suggest...

This paper concerns the determination of dose plan for prostate cancer in radiotherapy. The determination of dose plan is based on the analysis of trade-off between the expected benefits in terms of the cancer control and the risk in terms of the side effects to rectum caused by the given radiation. A casebased reasoning (CBR) approach to dose planning is proposed, in which fuzzy sets theory is...

The evidential reasoning (ER) approach is a method for multiple attribute decision analysis (MADA) under uncertainties. It improves the insightfulness and rationality of a decision making process by using a belief decision matrix (BDM) for problem modelling and the Dempster–Shafer (D–S) theory of evidence for attribute aggregation. The D– S theory provides scope and flexibility to deal with int...

In order to enhance the precision in association rule mining, an extension is proposed to capture the uncertain item relationships in the data sets in this paper. Two sources of uncertainty are considered: the degree of individual item importance (multiplicity) and the degree of association among the items (inter-relationship). In many real-world applications, especially in a distributed enviro...

We introduce a method for automatically labelling edges of word co-occurrence graphs with semantic relations. Therefore we only make use of training data already contained within the graph. Starting point of this work is a graph based on word co-occurrence of the German language, which is created by applying iterated co-occurrence analysis. The edges of the graph have been partially annotated b...