Nautical Knowledge Extraction and Decision Making

• Autorzy: Filipowicz Włodzimierz

Identyfikatory

DOI

10.12716/1001.19.02.32

• Języki publikacji: EN

Abstrakty

EN

One frequently encountered decision-making problem is the evaluation that boils down to judging hypotheses. Typically, we determine whether they are true or false, although we may also have doubts. Hypotheses can be statements of various kinds. For example, we may wish to classify a given student as belonging to the category of good students. Mentioned hypotheses are related to different disciplines, quite often seemingly uncorrelated. To confirm this hypothesis, we would most often refer to the subjective opinions of their teachers. A similar issue arises in nautical science; for instance, consider the problem of identifying a particular location as the most probable one where an observer is situated. Accompanied establishing ranges of the true, false and uncertain statement might be subjective. Objectivity could be also considered provided stored sets of instances are available. Expected are adequate functionalities of software tools at hand. Functional aspects tends to increase nowadays. Random observations are usually accompanied by methods rectifying knowledge regarding their behaviour and quality. Available data are explored in order to extract necessary parameters required within the inference schemes of evaluating the hypothesis truth.

Słowa kluczowe

EN

position fixing; positioning accuracy; navigational data reliability; sensor fusion; uncertainty analysis; plausibility measures; ship positioning; error analysis

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2025
• Tom: Vol. 19 No. 2
• Strony: 599--605
• Opis fizyczny: Bibliogr. 9 poz., rys., tab.

Twórcy

autor

Filipowicz Włodzimierz

• Gdynia Maritime University, Gdynia, Poland

• https://orcid.org/0000-0002-9713-8780

Bibliografia

• [1] Cuzzolin F.: The Geometry of Uncertainty: The Geometry of Imprecise Probabilities (Artificial Intelligence: Foundations, Theory, and Algorithms). Springer, 2021. ISBN-10: 3030631524
• [2] Filipowicz W.: Conditional dependencies in imprecise data handling KES’2021 Szczecin 25th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2021), organised by KES International and held in Szczecin, Poland
• [3] Filipowicz W.: Conditional Dependencies and Position Fixing. Appl. Sci. (ISSN 2076-3417) 2022, 12, 12324. https:// doi.org/10.3390/app1223123241
• [4] Filipowicz W.: Position Fixing and Uncertainty. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 17, No. 4, doi:10.12716/1001.17.04.15, pp. 887-893, 2023
• [5] Hau H.Y., Kashyap R.L.: Belief combination and propagation in a lattice-structured inference network. IEEE Transactions on Systems, Man and Cybernetics vol. 20, s. 45-57, 1990 DOI: 10.1109/21.47808
• [6] Lee E. S., Zhu Q.: Fuzzy and Evidence Reasoning. Physica-Verlag, Heidelberg (1995)
• [7] Liu W., Hughes J.G., McTear, M.F.: Representing Heuristic Knowledge and Propagating Beliefs in Dempster-Shafer Theory of Evidence. In: Federizzi, M., Kacprzyk, J., Yager, R.R. (eds.) Advances in the Dempster-Shafer Theory of Evidence. John Willey and Sons, Inc., New York 1992
• [8] Liu W., Hughes J.G., McTear, M.F.: Representing Heuristic Knowledge in D-S Theory. In: UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann Publishers Inc. San Francisco 1992
• [9] Trzaskalik T., Wielokryterialne wspomaganie decyzji – Metody i zastosowania , Polskie Wydawnictwo Ekonomiczne, Warszawa, 2014

Uwagi

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