Autonomous Discovery of Abstract Concepts by a Robot

  • Ivan Bratko
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6593)

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.

Keywords

autonomous discovery robot learning discovery of abstract concepts inductive logic programming predicate invention 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    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)Google Scholar
  2. 2.
    Bratko, I.: An Assessment of Machine Learning Methods for Robotic Discovery. Journal of Computing and Information Technology – CIT 16, 247–254 (2008)CrossRefGoogle Scholar
  3. 3.
    Demšar, J., Zupan, B.: Orange: Data Mining Fruitful & Fun - From Experimental Machine Learning to Interactive Data Mining (2006), http://www.ailab.si/orange
  4. 4.
    Šuc, D.: Machine Reconstruction of Human Control Strategies. In: Frontiers Artificial Intelligence Appl., vol. 99, IOS Press, Amsterdam (2003)Google Scholar
  5. 5.
    Križman, V.: Automatic Discovery of the Structure of Dynamic System Models. PhD thesis, Faculty of Computer and Information Sciences, University of Ljubljana (1998)Google Scholar
  6. 6.
    Srinivasan, A.: The Aleph Manual. Technical Report, Computing Laboratory, Oxford University (2000), http://web.comlab.ox.ac.uk/oucl/research/areas/machlearn/Aleph/
  7. 7.
    Bratko, I.: Prolog Programming for Artificial Intelligence, 3rd edn. Addison-Wesley / Pearson (2001)Google Scholar
  8. 8.
    Richardson, M., Domingos, P.: Markov Logic Networks. Machine Learning 62, 107–136 (2006)CrossRefGoogle Scholar
  9. 9.
    Dietterich, T.G., Domingos, P., Getoor, L., Muggleton, S., Tadepalli, P.: Structured machine learning: the next ten years. Machine Learning 73, 3–23 (2008)CrossRefGoogle Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    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)Google Scholar
  12. 12.
    Garcia-Martinez, R., Borrajo, D.: An integrated approach of learning, planning and execution. Journal of Intelligent and Robotic Systems 29, 47–78Google Scholar
  13. 13.
    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)Google Scholar
  14. 14.
    Zimmerman, T.L., Kambhampati, S.: Learning-assisted automated planning: Looking back, taking stock, going forward. AI Magazine 24(2), 73–96 (2003)Google Scholar
  15. 15.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Pearson, London (2009)MATHGoogle Scholar
  16. 16.
    De Raedt, L.: Logical and Relational Learning. Springer, Heidelberg (2008)CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ivan Bratko
    • 1
  1. 1.Faculty of Computer and Information Sc.University of LjubljanaLjubljanaSlovenia

Personalised recommendations