Abstract
In this paper we consider autonomous robot discovery through experimentation in the robot’s environment. We analyse the applicability of machine learning (ML) methods with respect to various levels of robot discovery tasks, from extracting simple laws among the observed variables, to discovering completely new notions that were never explicitly mentioned in the data directly. We first present some illustrative experiments in robot learning in the XPERO European project. Then we formulate criteria for a comparison of learning methods and a systematic list of types of learning or discovery tasks, and discuss the suitability of chosen ML methods for these tasks.
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Bratko, I. (2010). Comparison of Machine Learning for Autonomous Robot Discovery. In: Koronacki, J., Raś, Z.W., Wierzchoń, S.T., Kacprzyk, J. (eds) Advances in Machine Learning I. Studies in Computational Intelligence, vol 262. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05177-7_22
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DOI: https://doi.org/10.1007/978-3-642-05177-7_22
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