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Information Fusion
Volume 7, Issue 4, December 2006, Pages 361-379
Special Issue on the Seventh International Conference on Information Fusion-Part I, Seventh International Conference on Information Fusion
 
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doi:10.1016/j.inffus.2005.06.005    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2005 Elsevier B.V. All rights reserved.

Modeling partially reliable information sources: A general approach based on Dempster–Shafer theorystar, open, star, openstar, open

Rolf Haennia, E-mail The Corresponding Author, E-mail The Corresponding Author and Stephan Hartmannb, c, Corresponding Author Contact Information, E-mail The Corresponding Author

aInstitute of Computer Science and Applied Mathematics, University of Berne, CH-3012 Berne, Switzerland bDepartment of Philosophy, London School of Economics, London WC2A 2AE, United Kingdom cCenter for Junior Research Fellows, University of Konstanz, D-78457 Konstanz, Germany

Received 30 October 2004; 
revised 30 June 2005; 
accepted 30 June 2005. 
Available online 28 November 2005.

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Abstract

Combining testimonial reports from independent and partially reliable information sources is an important epistemological problem of uncertain reasoning. Within the framework of Dempster–Shafer theory, we propose a general model of partially reliable sources, which includes several previously known results as special cases. The paper reproduces these results on the basis of a comprehensive model taxonomy. This gives a number of new insights and thereby contributes to a better understanding of this important application of reasoning with uncertain and incomplete information.

Keywords: Dempster–Shafer theory; Probabilistic argumentation; Combining testimonial reports; Unreliable sources; Sensor fusion; Judgment aggregation; Probabilistic algorithms

Article Outline

1. Introduction
1.1. Existing models
1.1.1. Approach No. 1
1.1.2. Approach No. 2
1.1.3. Approach No. 3
1.1.4. Approach No. 4
1.2. Goals and overview
2.The general model
2.1. The general case
2.2. Identical sources
2.3. Special cases of prior knowledge
3. Taxonomy of models
3.1. Overview
3.2. Complete models
3.3. Incomplete models
3.4. Reliability-based models
3.5. Computing the parameters
3.6. Modeling prior knowledge
4. Case studies
4.1. The model (CPI)
4.2. The model (CL)
4.3. The model (CD)
4.4. The model (CPR)
4.5. The model (CYR)
5. Conclusion
Acknowledgements
References





Information Fusion
Volume 7, Issue 4, December 2006, Pages 361-379
Special Issue on the Seventh International Conference on Information Fusion-Part I, Seventh International Conference on Information Fusion
 
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