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Online Structure Learning for Traffic Management

  • Evangelos MichelioudakisEmail author
  • Alexander Artikis
  • Georgios Paliouras
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10326)

Abstract

Most event recognition approaches in sensor environments are based on manually constructed patterns for detecting events, and lack the ability to learn relational structures in the presence of uncertainty. We describe the application of \(\mathtt {OSL}\alpha \), an online structure learner for Markov Logic Networks that exploits Event Calculus axiomatizations, to event recognition for traffic management. Our empirical evaluation is based on large volumes of real sensor data, as well as synthetic data generated by a professional traffic micro-simulator. The experimental results demonstrate that \(\mathtt {OSL}\alpha \) can effectively learn traffic congestion definitions and, in some cases, outperform rules constructed by human experts.

Keywords

Markov Logic Networks Event Calculus Uncertainty 

Notes

Acknowledgments

Funded by EU FP7 project SPEEDD (619435).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Evangelos Michelioudakis
    • 1
    Email author
  • Alexander Artikis
    • 2
    • 1
  • Georgios Paliouras
    • 1
  1. 1.Institute of Informatics and TelecommunicationsNCSR “Demokritos”Agia ParaskeviGreece
  2. 2.Department of Maritime StudiesUniversity of PiraeusPiraeusGreece

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