Discovery of Abstract Concepts by a Robot

  • Ivan Bratko
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6332)

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.

Keywords

Autonomous discovery robot learning discovery of abstract concepts inductive logic programming 

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’07 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, 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.
    Aldebaran robotics – Nao (2010), http://www.aldebaran-robotics.com/eng/index.php
  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.
    Košmerlj, A., Leban, G., Žabkar, J., Bratko, I.: Gaining Insights About Objects Functions, Properties and Interactions, XPERO Report D4.3. Univ. of Ljubljana, Faculty of Computer and Info. Sc. (2009)Google Scholar
  12. 12.
    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

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

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

Personalised recommendations