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Extending adaptive world modeling by identifying and handling insufficient knowledge models

 
: Kuwertz, Achim; Beyerer, Jürgen

:

Journal of applied logic 19 (2016), Pt.2, S.102-127
ISSN: 1570-8683 (print)
Englisch
Zeitschriftenaufsatz
Fraunhofer IOSB ()
probabilistic world modeling; adaptive knowledge management; object oriented methods; object recognition; Concept learning; cognitive robotics

Abstract
Adaptive knowledge modeling is an approach for extending the abilities of the Object-Oriented World Model, a system for representing the state of an observed real-world environment, to open-world modeling. In open environments, entities unforeseen at the design-time of a world model can occur. For coping with such circumstances, adaptive knowledge modeling is tasked with adapting the underlying knowledge model according to the environment. The approach is based on quantitative measures, introduced previously, for rating the quality of knowledge models. In this contribution, adaptive knowledge modeling is extended by measures for detecting the need for model adaptation and identifying the potential starting points of necessary model change as well as by an approach for applying such change. Being an extended and more detailed version of [17], the contribution also provides background information on the architecture of the Object-Oriented World Model and on the principles of adaptive knowledge modeling, as well as examination results for the proposed methods. In addition, a more complex scenario is used to evaluate the overall approach.

: http://publica.fraunhofer.de/dokumente/N-423003.html