Abstract
This paper reviews experiments with an approach to discovery through robot’s experimentation in its environment. In addition to discovering laws that enable predictions, we are particularly interested in the mechanisms that enable the discovery of abstract concepts that are not explicitly observable in the measured data, such as the notions of a tool or stability. The approach is based on the use of Inductive Logic Programming. Examples of actually discovered abstract concepts in the experiments include the concepts of a movable object, an obstacle and a tool.
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© 2010 Springer-Verlag Berlin Heidelberg
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Bratko, I. (2010). Discovery of Abstract Concepts by a Robot. In: Hutter, M., Stephan, F., Vovk, V., Zeugmann, T. (eds) Algorithmic Learning Theory. ALT 2010. Lecture Notes in Computer Science(), vol 6331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16108-7_4
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DOI: https://doi.org/10.1007/978-3-642-16108-7_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16107-0
Online ISBN: 978-3-642-16108-7
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