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Interleaved Inductive-Abductive Reasoning for Learning Complex Event Models

  • Krishna Dubba
  • Mehul Bhatt
  • Frank Dylla
  • David C. Hogg
  • Anthony G. Cohn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7207)

Abstract

We propose an interleaved inductive-abductive model for reasoning about complex spatio-temporal narratives. Typed Inductive Logic Programming (Typed-ILP) is used as a basis for learning the domain theory by generalising from observation data, whereas abductive reasoning is used for noisy data correction by scenario and narrative completion thereby improving the inductive learning to get semantically meaningful event models. We apply the model to an airport domain consisting of video data for 15 turn-arounds from six cameras simultaneously monitoring logistical processes concerned with aircraft arrival, docking, departure etc and a verbs data set with 20 verbs enacted out in around 2500 vignettes. Our evaluation and demonstration focusses on the synergy afforded by the inductive-abductive cycle, whereas our proposed model provides a blue-print for interfacing common-sense reasoning about space, events and dynamic spatio-temporal phenomena with quantitative techniques in activity recognition.

Keywords

Logic Programming Inductive Logic Programming Situation Calculus Bottom Clause Commonsense Reasoning 
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 2012

Authors and Affiliations

  • Krishna Dubba
    • 1
  • Mehul Bhatt
    • 2
  • Frank Dylla
    • 2
  • David C. Hogg
    • 1
  • Anthony G. Cohn
    • 1
  1. 1.School of ComputingUniversity of LeedsUK
  2. 2.SFB/TR 8 Spatial CognitionUniversity of BremenGermany

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