Abstract
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.
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Skarlatidis, A., Paliouras, G., Vouros, G.A., Artikis, A. (2011). Probabilistic Event Calculus Based on Markov Logic Networks. In: Olken, F., Palmirani, M., Sottara, D. (eds) Rule-Based Modeling and Computing on the Semantic Web. RuleML 2011. Lecture Notes in Computer Science, vol 7018. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24908-2_19
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DOI: https://doi.org/10.1007/978-3-642-24908-2_19
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