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., Šuc, D., Awaad, I., Demšar, J., Gemeiner, P., Guid, M., Leon, B., Mestnik, M., Prankl, J., Prassler, E., Vincze, M., Žabkar, J.: Initial experiments in robot discovery in XPERO. In: ICRA 2007 Workshop Concept Learning for Embodied Agents, Rome (2007)
Bratko, I.: An Assessment of Machine Learning Methods for Robotic Discovery. Journal of Computing and Information Technology – CIT 16, 247–254 (2008)
Demšar, J., Zupan, B.: Orange: Data Mining Fruitful & Fun - From Experimental Machine Learning to Interactive Data Mining (2006), http://www.ailab.si/orange
Šuc, D.: Machine Reconstruction of Human Control Strategies. In: Frontiers Artificial Intelligence Appl., vol. 99, IOS Press, Amsterdam (2003)
Križman, V.: Automatic Discovery of the Structure of Dynamic System Models. PhD thesis, Faculty of Computer and Information Sciences, University of Ljubljana (1998)
Srinivasan, A.: The Aleph Manual. Technical Report, Computing Laboratory, Oxford University (2000), http://web.comlab.ox.ac.uk/oucl/research/areas/machlearn/Aleph/
Bratko, I.: Prolog Programming for Artificial Intelligence, 3rd edn. Addison-Wesley / Pearson (2001)
Richardson, M., Domingos, P.: Markov Logic Networks. Machine Learning 62, 107–136 (2006)
Dietterich, T.G., Domingos, P., Getoor, L., Muggleton, S., Tadepalli, P.: Structured machine learning: the next ten years. Machine Learning 73, 3–23 (2008)
Leban, G., Žabkar, J., Bratko, I.: An experiment in robot discovery with ILP. In: Železný, F., Lavrač, N. (eds.) ILP 2008. LNCS (LNAI), vol. 5194, pp. 77–90. Springer, Heidelberg (2008)
Stahl, I.: Predicate invention in Inductive Logic Programming. In: De Raedt, L. (ed.) Advances in Inductive Logic Programming, pp. 34–47. IOS Press, Amsterdam (1996)
Garcia-Martinez, R., Borrajo, D.: An integrated approach of learning, planning and execution. Journal of Intelligent and Robotic Systems 29, 47–78
Veloso, M., Carbonell, J., Perez, A., Borrajo, D., Fink, E., Blythe, J.: Integrating planning and learning. J. of Experimental and Theoretical AI 7(1) (1995)
Zimmerman, T.L., Kambhampati, S.: Learning-assisted automated planning: Looking back, taking stock, going forward. AI Magazine 24(2), 73–96 (2003)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Pearson, London (2009)
De Raedt, L.: Logical and Relational Learning. Springer, Heidelberg (2008)
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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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