Summary
The main goal of information fusion can be seen as improving human or automatic decision-making by exploiting diversities in information from multiple sources. High-level information fusion aims specifically at decision support regarding situations, often expressed as “achieving situation awareness”. A crucial issue for decision making based on such support is trust that can be defined as “accepted dependence”, where dependence or dependability is an overall term for many other concepts, e.g., reliability. This position paper reports on ongoing and planned research concerning imprecise probability as an approach to improved dependability in high-level information fusion. We elaborate on high-level information fusion from a generic perspective and a partial mapping from a taxonomy of dependability to high-level information fusion is presented. Three application domains: defense, manufacturing, and precision agriculture, where experiments are planned to be implemented are depicted. We conclude that high-level information fusion as an application-oriented research area, where precise probability (Bayesian theory) is commonly adopted, provides an excellent evaluation ground for imprecise probability.
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References
Adamchuk, V.I., et al.: On-the-go soil sensors for precision agriculture. Computers and electronics in agriculture 44, 71–91 (2004)
Antonucci, A., et al.: Credal networs for military identification problems. In: 5th International Symposium on Imprecise Probability: Theories and application (2007)
Antony, R.: Data fusion automation: A top-down perspective. In: Hall, D.L., Llinas, J. (eds.) Handbook of Multisensor Data Fusion, pp. 6-1–6-25. CRC Press, Boca Raton (2001)
Aughenbaugh, J.M.: Managing uncertainty in engineering design using imprecise probabilities and principles of information economics. PhD thesis, Georgia Institute of Technology (2006)
Avižienis, A., et al.: Basic concepts and taxonomy of dependable and secure computing. IEEE Transactions on dependable and secure computing 1, 11–33 (2004)
Bladon, P., Hall, R.J., Wright, W.A.: Situation assessment using graphical models. In: Proceedings of the 5th International Conference on Information fusion (2002)
Bloch, I., et al.: Fusion: General concepts and characteristics. International Journal of Intelligent Systems 16, 1107–1134 (2001)
Borotschnig, H., et al.: A comparison of probabilistic, possibilistic and evidence theoretic fusion schemes for active object recognition. Computing 62, 293–319 (1999)
Bossé, É., Guitouni, A., Valin, P.: An essay to characterise information fusion systems. In: Proceedings of the 9th International Conference on Information fusion (2006)
Cozman, F.G.: Credal networks. Artificial intelligence 120, 199–233 (2000)
Cozman, F.G.: Graphical models for imprecise probabilities. International Journal of Approximate Reasoning 39, 167–184 (2005)
Das, S., Lawless, D.: Trustworthy situation assessment via belief networks. In: Proceedings of the 5th International Conference on Information fusion (2002)
Dasarathy, B.V.: Information fusion - what, where, why, when, and how? Information Fusion 2, 75–76 (2001)
Devlin, K.: Logic and Information. Cambridge university press, Cambridge (1995)
Ferson, S., et al.: Summary from the epistemic uncertainty workshop: consensus amid diversity. Reliability Engineering & System Safety 85, 355–369 (2004)
Hall, D.L., Llinas, J.: Multisensor data fusion. In: Hall, D.L., Llinas, J. (eds.) Handbook of Multisensor Data Fusion, pp. 1-1–1-10. CRC Press, Boca Raton (2001)
Heckerman, D.: Bayesian networks for data mining. Data Mining and Knowledge Discovery 1, 79–119 (1997)
Hinman, M.L.: Some computational approaches for situation assessment and impact assessment. In: Proceedings of the 5th International Conference on Information fusion (2002)
Jardine, A.K.S., Lin, D., Banjevic, D.: A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing 20, 1483–1510 (2006)
Jaynes, E.T.: Information theory and statistical mechanics. The Physical Review 106(4), 620–630 (1957)
Johansson, F., Falkman, G.: Implementation and integration of a Bayesian network for prediction of tactical intention into a ground target simulator. In: Proceedings of the 9th International Conference on Information fusion (2006)
Karlsson, A.: Dependable and generic high-level information fusion - methods and algorithms for uncertainty management. Tech. Rep. HS-IKI-TR-07-003, School of Humanities and Informatics, University of Skövde, Sweden (2007)
Kokar, M.M.: Situation awareness: Issues and challenges, in panel discussion: Higher level fusion: Unsolved, difficult, and misunderstood problems/approaches in levels 2-4 fusion research. In: Proceedings of the 7th International Conference on Information fusion (2004)
Kokar, M.M., Matheus, C.J., Baclawski, K.: Ontology-based situation awareness. Information fusion (2007), doi:10.1016/j.inffus.2007.01.004
Llinas, J.: Assessing the performance of multisensor fusion processes. In: Hall, D.L., Llinas, J. (eds.) Handbook of Multisensor Data Fusion, pp. 20-1–20-18. CRC Press, Boca Raton (2001)
Llinas, J., et al.: Revisiting the JDL data fusion model II. In: Proceedings of the Seventh International Conference on Information Fusion (2004)
Looney, C.G., Liang, L.R.: Cognitive situation and threat assessment of ground battlesspaces. Information Fusion 4, 297–308 (2003)
Mcbratney, A., Whelan, B., Ancev, T.: Future directions of precision agriculture. Precision Agriculture 6, 7–23 (2005)
Roemer, M.J., Kacprzynski, G.J., Orsagh, R.F.: Assessment of data and knowledge fusion strategies for prognostics and health management. In: IEEE Aerospace Conference Proceedings (2001)
Rogova, G.L., Nimier, V.: Reliability in information fusion: Literature survey. In: Proceedings of the 7th International Conference on Information fusion (2004)
Rogova, G.L., et al.: Reasoning about situations in the early post-disaster response environment. In: Proceedings of the 9th International Conference on Information fusion (2006)
Schubert, J.: Evidential force aggregation. In: Proceedings of the 6th International Conference on Information Fusion (2003)
Soundappan, P., et al.: Comparison of evidence theory and Bayesian theory for uncertainty modeling. Reliability engineering & System safety 85, 295–311 (2004)
Stafford, J.V.: Implementing precision agriculture in the 21st century. Journal of Agricultural Engineering Research 76, 267–275 (2000)
Steinberg, A.N., Bowman, C.L., White, F.E.: Revisions to the JDL data fusion model. In: Dasarathy, B.V. (ed.) Proceedings of SPIE - Sensor fusion: Architectures, Algorithms, and Applications III, vol. 3719 (1999)
Svensson, P.: On reliability and trustworthiness of high-level fusion-based decision support systems: Basic concepts and possible formal methodologies. In: Proceedings of the 9th International Conference on Information fusion (2006)
Thöne, H., Güntzer, U., Kießling, W.: Increased robustness of Bayesian networks through probability intervals. International Journal of Approximate Reasoning 17, 37–76 (1997)
Vin, L.J.D., et al.: Information fusion for simulation based decision support in manufacturing. Robotics and Computer-Integrated Manufacturing 22, 429–436 (2006)
Walley, P.: Statistical Reasoning with Imprecise Probabilities. Chapman and Hall, Boca Raton (1991)
Walley, P.: Measures of uncertainty in expert systems. Artificial Intelligence 83, 1–58 (1996)
Walley, P.: Towards a unified theory of imprecise probability. International Journal of Approximate Reasoning 24, 125–148 (2000)
Wang, P.: Confidence as higher-order uncertainty. In: 2nd International Symposium on Imprecise Probabilities and Their Applications (2001)
White Jr., F.E.: Data fusion Lexicon. Joint Directors of Laboratories, Technical panel for C 3, Data Fusion Sub-Panel, Naval Ovean Systems Center, San Diego (1987)
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Karlsson, A., Johansson, R., Andler, S.F. (2008). Imprecise Probability as an Approach to Improved Dependability in High-Level Information Fusion. In: Huynh, VN., Nakamori, Y., Ono, H., Lawry, J., Kreinovich, V., Nguyen, H.T. (eds) Interval / Probabilistic Uncertainty and Non-Classical Logics. Advances in Soft Computing, vol 46. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77664-2_7
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DOI: https://doi.org/10.1007/978-3-540-77664-2_7
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