Advertisement

Abducing Hypotheses About Past Events from Observed Environment Changes

  • Ann-Katrin Becker
  • Jochen Sprickerhof
  • Martin Günther
  • Joachim Hertzberg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9324)

Abstract

Humans perform abductive reasoning routinely. We hypothesize about what happened in the past to explain an observation made in the present. This is frequently needed to model the present, too.

In this paper we describe an approach to equip robots with the capability to abduce hypotheses triggered by unexpected observations from sensor data. This is realized on the basis of KnowRob, which provides general knowledge about objects and actions. First we analyze the types of environment changes that a robot may encounter. Thereafter we define new reasoning methods allowing to abduce past events from observed changes. By projecting the effects of these hypothetical previous events, the robot gains knowledge about consequences likely to expect in its present. The applicability of our reasoning methods is demonstrated in a virtual setting as well as in a real-world scenario. In these, our robot was able to abduce highly probable information not directly accessible from its sensor data.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lehmann, D., Magidor, M.: Preferential logics: the predicate calculus case. In: Proc. 3rd. Theoretical Aspects of Reasoning about Knowledge, pp. 57–72 (1980)Google Scholar
  2. 2.
    Mason, J., Marthi, B.: An object-based semantic world model for long-term change detection and semantic querying. In: Proc. IROS, pp. 3851–3858 (2012)Google Scholar
  3. 3.
    Mason, J., Marthi, B., Parr, R.: Object disappearance for object discovery. In: Proc. IROS, pp. 2836–2843 (2012)Google Scholar
  4. 4.
    Nitti, D., Laet, T.D., Raedt, L.D.: A particle filter for hybrid relational domains. In: Proc. IROS, pp. 2764–2771 (2013)Google Scholar
  5. 5.
    Nitti, D., Laet, T.D., Raedt, L.D.: Relational object tracking and learning. In: Proc. IROS (2014)Google Scholar
  6. 6.
    Reiter, R.: A logic for default reasoning. Artificial Intelligence 13(1), 81–132 (1980)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Sterling, L., Shapiro, E.: The Art of Prolog: Advanced Programming Techniques. MIT Press (1994)Google Scholar
  8. 8.
    Tenorth, M., Beetz, M.: A unified representation for reasoning about robot actions, processes, and their effects on objects. In: Proc. IROS (2012)Google Scholar
  9. 9.
    Tenorth, M., Beetz, M.: KnowRob: A knowledge processing infrastructure for cognition-enabled robots. Int. J. of Robotics Research 32 (2013)Google Scholar
  10. 10.
    Wang, D., Federspiel, C.C., Rubinstein, F.: Modeling occupancy in single person offices. Energy and Buildings 37(2), 121–126 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ann-Katrin Becker
    • 1
  • Jochen Sprickerhof
    • 1
  • Martin Günther
    • 1
    • 2
  • Joachim Hertzberg
    • 1
    • 2
  1. 1.Knowledge Based Systems GroupOsnabrück UniversityOsnabrückGermany
  2. 2.DFKI Robotics Innovation CenterOsnabrückGermany

Personalised recommendations