On Learning spatio-temporal relational structures in two different domains

  • Adrian R. Pearce
  • Terry Caelli
  • Simon Goss
Poster Session III
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1352)


In this paper we consider the types of representations and learning procedures required to construct rules which can adequately describe relational information as it occurs in spatio-temporal sequences. A comparison of interpreting on-line hand drawings is made to the automatic generation of flight manoeuvre description based on a relational learning system we have developed, the Consolidated Learning Algorithm based on Relational Evidence Theory (CLARET). The package adapts relational learning techniques to utilise the constraints present in time series data. Our approach involves supporting queries, automatic descriptions and/or predictions from spatio-temporal action sequences.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Adrian R. Pearce
    • 1
  • Terry Caelli
    • 1
  • Simon Goss
    • 2
  1. 1.School of ComputingCurtin UniversityPerthAustralia
  2. 2.Aeronautical and Maritime Research LaboratoriesDSTOMelbourneAustralia

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