NDLI: A Decision Support Model for Uncertainty Reasoning in Safety and Security Tasks

Content Provider	IEEE Xplore Digital Library
Author	Michels, S. Velikova, M. Hommersom, A. Lucas, P.J.F.
Copyright Year	2013
Description	Author affiliation: Inst. for Comput. & Inf. Sci., Radboud Univ. Nijmegen, Nijmegen, Netherlands (Michels, S.; Velikova, M.; Hommersom, A.; Lucas, P.J.F.) Performing safety and security tasks requires the continuous gathering and interpretation of information about objects to detect and predict events of interest. Especially, reasoning about objects' identities and intentions is crucial, but requires making use of heterogeneous information inherent with uncertainty. This makes such tasks very challenging for human operators and decision support systems are clearly needed. In this paper, we present a generic probabilistic logic model for the systematic representation and uncertainty reasoning about the identity and intentions of monitored objects. We apply the model to the area of maritime safety and security after incorporating domain specific knowledge. Experiments with simulated and real-world vessel data demonstrate the model's capabilities to draw conclusions about objects using uncertain information. The first-order probabilistic logic we use shows to be a very powerful tool to deal with dynamic amounts of information and objects. To our knowledge it is one of the few real-world applications of probabilistic logic.
Sponsorship	IEEE Syst., Man, Cybern. Soc.
Starting Page	663
Ending Page	668
File Size	523450
Page Count	6
File Format	PDF
ISBN	9781479906529
DOI	10.1109/SMC.2013.118
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-13
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Error analysis Marine vehicles Probabilistic logic Decision support systems Uncertainty Safety Security maritime safety and security tasks decision support uncertainty probabilistic logic
Content Type	Text
Resource Type	Article

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