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Relational Sequence Learning

  • Kristian Kersting
  • Luc De Raedt
  • Bernd Gutmann
  • Andreas Karwath
  • Niels Landwehr
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4911)

Abstract

Sequential behavior and sequence learning are essential to intelligence. Often the elements of sequences exhibit an internal structure that can elegantly be represented using relational atoms. Applying traditional sequential learning techniques to such relational sequences requires one either to ignore the internal structure or to live with a combinatorial explosion of the model complexity. This chapter briefly reviews relational sequence learning and describes several techniques tailored towards realizing this, such as local pattern mining techniques, (hidden) Markov models, conditional random fields, dynamic programming and reinforcement learning.

Keywords

Hide Markov Model Markov Decision Process Relational Sequence Ground Atom Block World 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Kristian Kersting
    • 1
  • Luc De Raedt
    • 2
  • Bernd Gutmann
    • 2
  • Andreas Karwath
    • 3
  • Niels Landwehr
    • 3
  1. 1.CSAILMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.Departement ComputerwetenschappenK.U. LeuvenHeverleeBelgium
  3. 3.Machine Learning Lab, Institute for Computer ScienceUniversity of Freiburg, Georges-Koehler AlleeFreiburgGermany

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