Fusion 2016 Special Session on
Evaluation of Technologies for Uncertainty Reasoning

Table of Contents:

• Organizers
• Description of the Special Session
• Research Topics that will Develop
• List of Papers/Presentations

Organizers

Paulo Costa                      George Mason University, Fairfax, VA, USA
Kathryn Laskey                 George Mason University, Fairfax, VA, USA
Anne-Laure Jousselme     NATO CMRE, La Spezia, Italy
Erik Blasch                        AFRL, Rome, NY, USA
Jüergen Ziegler                 IABG, Ottobrunn, Germany
Valentina Dragos              ONERA, France
Pieter DeVilliers                University of Pretoria, Pretoria, South Africa

Gregor Pavlin                    D-CIS Lab, Thales R&T, Delft, The Netherlands 

Description of the Special Session

The ETUR Session is intended to report the latest results of the ISIF’s ETURWG, which aims to bring together advances and developments in the area of evaluation of uncertainty representation. The ETURWG special sessions started in Fusion 2010 and has been held ever since, which an attendance consistently averaging between 40 and 48 attendees. While most attendees consist of ETURWG participants new researchers and practitioners interested in uncertainty evaluation have attended the sessions and some stayed with the ETURWG.

Research Topics that will Develop

The session will focus three topics:
(1) to summarize the state of the art in uncertainty analysis, representation, and evaluation,
(2) discussion of metrics for uncertainty representation, and
(3) survey uncertainty at all levels of fusion.

The impact to the ISIF community would be an organized session with a series of methods in uncertainty representation as coordinated with evaluation. The techniques discussed and questions/answers would be important for the researchers in the ISIF community; however, the bigger impact would be for the customers of information fusion systems to determine how measure, evaluate, and approve systems that assess the situation beyond Level 1 fusion.

The customers of information fusion products would have some guidelines to draft requirements documentation, the gain of fusion systems over current techniques, as well as issues that important in information fusion systems designs. One of the main goals of information fusion is uncertainty reduction, which is dependent on the representation chosen. Uncertainty representation differs across the various levels of Information Fusion (as defined by the JDL/DFIG models). Given the advances in information fusion systems, there is a need to determine how to represent and evaluate situational (Level 2 Fusion), impact (Level 3 Fusion) and process refinement (Level 5 Fusion), which is not well standardized for the information fusion community.

List of Papers / Presentations

1. Pragmatic data fusion uncertainty concerns: Tribute to Dave L. Hall.
   Blasch, E., Costa, P. C. G., De Villiers, J. P., Laskey, K. B., Llinas, J., & Jousselme, A.-L.
   https://ieeexplore.ieee.org/document/7527926

2. de Villiers, J. P., Jousselme, A.-L., de Waal, A., Pavlin, G., Laskey, K., Blasch, E., & Costa, P. (2016). Uncertainty evaluation of data and information fusion within the context of the decision loop. 2016 19th International Conference on Information Fusion (FUSION), 766–773. https://ieeexplore.ieee.org/document/7527964

3. de Waal, A., Koen, H., de Villiers, P., Roodt, H., Moorosi, N., & Pavlin, G. (2016). Construction and evaluation of Bayesian networks with expert-defined latent variables. 2016 19th International Conference on Information Fusion (FUSION), 774–781. https://ieeexplore.ieee.org/document/7527965

4. Dezert, J., Han, D., & Yin, H. (2016, July). A New Belief Function Based Approach for Multi-Criteria Decision-Making Support. FUSION 2016.

5. Heendeni, J. N., Premaratne, K., Murthi, M. N., Uscinski, J., & Scheutz, M. (2016). A generalization of Bayesian inference in the Dempster-Shafer belief theoretic framework. 2016 19th International Conference on Information Fusion (FUSION), 798–804. https://ieeexplore.ieee.org/document/7527968

6. Josang, A. (2016, July). Decision Making Under Vagueness and Uncertainty. 2016 19th International Conference on Information Fusion (FUSION). 2016 19th International Conference on Information Fusion (FUSION).

7. Jousselme, A.-L. (2016). Semantic criteria for the assessment of Uncertainty handling fusion models. 2016 19th International Conference on Information Fusion (FUSION), 488–495. https://ieeexplore.ieee.org/document/7527928

8. Krüger, M., & Ziegler, J. (2016). A similarity measure in Bayesian classification based on characteristic attributes of objects. 2016 19th International Conference on Information Fusion (FUSION), 480–487. https://ieeexplore.ieee.org/abstract/document/7527927

9. Pavlin, G., Claessens, R., de Oude, P., & Costa, P. C. G. (2016). Evaluation of a canonical model approach to probabilistic data association in tracking with particle filters. 2016 19th International Conference on Information Fusion (FUSION), 464–471. https://ieeexplore.ieee.org/document/7527925

10. Rogova, G., & Yager, R. (2016). Belief-based argumentation and golden rule for decision making with soft and hard information. 2016 19th International Conference on Information Fusion (FUSION), 790–797. https://ieeexplore.ieee.org/document/7527967