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Parallel Online Learning of Event Definitions

  • Nikos KatzourisEmail author
  • Alexander Artikis
  • Georgios Paliouras
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10759)

Abstract

Logic-based event recognition systems infer occurrences of events in time using a set of event definitions in the form of first-order rules. The Event Calculus is a temporal logic that has been used as a basis in event recognition applications, providing among others, direct connections to machine learning, via Inductive Logic Programming (ILP). OLED is a recently proposed ILP system that learns event definitions in the form of Event Calculus theories, in a single pass over a data stream. In this work we present a version of OLED that allows for parallel, online learning. We evaluate our approach on a benchmark activity recognition dataset and show that we can reduce training times, while achieving super-linear speed-ups on some occasions.

Notes

Acknowledgments

This work is funded by the H2020 project datAcron (687591).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Nikos Katzouris
    • 1
    Email author
  • Alexander Artikis
    • 1
    • 2
  • Georgios Paliouras
    • 1
  1. 1.National Center for Scientific Research “Demokritos”AthensGreece
  2. 2.Department of Maritime StudiesUniversity of PiraeusPiraeusGreece

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