Abstract
In various domains, such as security and surveillance, a large amount of information from heterogeneous sources is continuously gathered to identify and prevent potential threats, but it is unknown in advance what the observed entity of interest should look like. The quality of the decisions made depends, of course, on the quality of the information they are based on. In this paper, we propose a novel method for assessing the quality of information taking into account uncertainty. Two properties – soundness and completeness – of the information are used to define the notion of information quality and their expected values are defined using a probabilistic model output. Simulation experiments with data from a maritime scenario demonstrates the usage of the proposed method and its potential for decision support in complex tasks such as surveillance.
This publication was supported by the Dutch national program COMMIT. The research work was carried out as part of the Metis project under the responsibility of the Embedded Systems Institute with Thales Nederland B.V. as the carrying industrial partner.
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Michels, S., Velikova, M., Lucas, P.J.F. (2012). Probabilistic Model-Based Assessment of Information Quality in Uncertain Domains. In: Thielscher, M., Zhang, D. (eds) AI 2012: Advances in Artificial Intelligence. AI 2012. Lecture Notes in Computer Science(), vol 7691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35101-3_75
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DOI: https://doi.org/10.1007/978-3-642-35101-3_75
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