Abstract
Threat evaluation is a high-level information fusion problem of high importance within the military domain. This task is the foundation for weapons allocation, where assignment of blue force (own) weapon systems to red force (enemy) targets is performed. In this paper, we compare two fundamentally different approaches to threat evaluation: Bayesian networks and fuzzy inference rules. We conclude that there are pros and cons with both types of approaches, and that a hybrid of the two approaches seems both promising and viable for future research.
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Roux, J.N., van Vuuren, J.H.: Threat evaluation and weapon assignment decision support: A review of the state of the art. ORiON 23, 151–186 (2007)
Roy, J., Paradis, S., Allouche, M.: Threat evaluation for impact assessment in situation analysis systems. In: Kadar, I. (ed.) Proceedings of SPIE: Signal Processing, Sensor Fusion, and Target Recognition XI, vol. 4729, pp. 329–341 (2002)
Paradis, S., Benaskeur, A., Oxenham, M., Cutler, P.: Threat evaluation and weapons allocation in network-centric warfare. In: Proceedings of the 8th International Conference on Information Fusion (2005)
Steinberg, A., Bowman, C., White, F.: Revisions to the JDL data fusion model. In: Proceedings of the SPIE Sensor Fusion: Architectures, Algorithms, and Applications III, pp. 430–441 (1999)
Phister, P.W., Plonisch, I.: Data fusion “cube”: A multi-dimensional perspective. In: Proceedings of the Command and Control Research and Technology Symposium (CCRTS 2002) (2002)
Johansson, F., Falkman, G.: A Bayesian network approach to threat evaluation with application to an air defense scenario. In: Proceedings of the 11th International Conference on Information Fusion (2008)
Liang, Y.: An approximate reasoning model for situation and threat assessment. In: Proceedings of the 4th International Conference on Fuzzy Systems and Knowledge Discovery (2007)
Liang, Y.: A fuzzy knowledge based system in situation and threat assessment. Journal of Systems Science & Information 4(4), 791–802 (2006)
Nguyen, X.: Threat assessment in tactical airborne environments. In: Proceedings of the Fifth International Conference on Information Fusion (2002)
Waltz, E.L., Llinas, J.: Multisensor Data Fusion. Artech House (1990)
Lauritzen, S.L., Spiegelhalter, D.J.: Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society B50, 157–224 (1988)
Huang, C., Darwiche, A.: Inference in belief networks: A procedural guide. International Journal of Approximate Reasoning 15(3), 225–263 (1996)
Klir, G.J., Folger, T.A.: Fuzzy sets, uncertainty, and information. Prentice-Hall, Inc., Upper Saddle River (1987)
Kruse, R., Gebhardt, J.E., Klowon, F.: Foundations of Fuzzy Systems. John Wiley & Sons, Inc., New York (1994)
Kreinovich, V., Nguyen, H.T.: Which fuzzy logic is the best: Pragmatic approach (and its theoretical analysis). Fuzzy Sets and Systems 157(5), 611–614 (2006)
Druzdzel, M., van der Gaag, L.: Building probabilistic networks: where do the numbers come from? IEEE Transactions on Knowledge and Data Engineering 12(4), 481–486 (2000)
Chan, H., Darwiche, A.: On the revision of probabilistic beliefs using uncertain evidence. Artificial Intelligence 163(1), 67–90 (2005)
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Johansson, F., Falkman, G. (2008). A Comparison between Two Approaches to Threat Evaluation in an Air Defense Scenario. In: Torra, V., Narukawa, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2008. Lecture Notes in Computer Science(), vol 5285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88269-5_11
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DOI: https://doi.org/10.1007/978-3-540-88269-5_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88268-8
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