Abstract
In this paper we look at the discovery of abstract concepts by a robot autonomously exploring its environment and learning the laws of the environment. By abstract concepts we mean concepts that are not explicitly observable in the measured data, such as the notions of obstacle, stability or a tool. We consider mechanisms of machine learning that enable the discovery of abstract concepts. Such mechanisms are provided by the logic based approach to machine learning called Inductive Logic Programming (ILP). The feature of predicate invention in ILP is particularly relevant. Examples of actually discovered abstract concepts in experiments are described.
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Bratko, I. (2011). Autonomous Discovery of Abstract Concepts by a Robot. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6593. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20282-7_1
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DOI: https://doi.org/10.1007/978-3-642-20282-7_1
Publisher Name: Springer, Berlin, Heidelberg
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