Probabilistic Event Calculus Based on Markov Logic Networks

  • Anastasios Skarlatidis
  • Georgios Paliouras
  • George A. Vouros
  • Alexander Artikis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7018)


In this paper, we address the issue of uncertainty in event recognition by extending the Event Calculus with probabilistic reasoning. Markov Logic Networks are a natural candidate for our logic-based formalism. However, the temporal semantics of Event Calculus introduce a number of challenges for the proposed model. We show how and under what assumptions we can overcome these problems. Additionally, we demonstrate the advantages of the probabilistic Event Calculus through examples and experiments in the domain of activity recognition, using a publicly available dataset of video surveillance.


Soft Constraint Event Recognition Symbolic Method Markov Logic Network Event Calculus 
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 2011

Authors and Affiliations

  • Anastasios Skarlatidis
    • 1
    • 2
  • Georgios Paliouras
    • 1
  • George A. Vouros
    • 2
  • Alexander Artikis
    • 1
  1. 1.Institute of Informatics and Telecommunications, NCSR “Demokritos”AthensGreece
  2. 2.Department of Information and Communication Systems EngineeringUniversity of the AegeanSamosGreece

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