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Knowledge model quantitative evaluation for adaptive world modeling

: Kuwertz, Achim; Beyerer, Jürgen

Preprint urn:nbn:de:0011-n-2497609 (567 KByte PDF) - other pagination
MD5 Fingerprint: 037ef678fb77e30bf3b52d91d3ece28e
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Erstellt am: 23.7.2013

IEEE Conference on Cognitive Methods in Situation Awareness and Decision Support, CogSIMA 2013 : 26-28 February, 2013, San Diego, CA, USA
New York, NY: IEEE, 2013
ISBN: 978-1-4673-2437-3 (Print)
International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA) <3, 2013, San Diego/Calif.>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
model evaluation; object-oriented world modeling; information-theoretic measures; minimum description length

World modeling can provide environment information to applications for decision support and situation assessment. In a semantic world model like the Object-Oriented World Model (OOWM), knowledge about an application domain is modeled a priori. In practice, however, world modeling systems have to deal with an open world, where unforeseen real-world entities can occur during operations. To enable open-world modeling for the OOWM, an approach to adaptive knowledge management is presented. This approach proposes an information-theoretic model evaluation based on the Minimum Description Length principle.