Probabilistic Event Calculus for Event Recognition

TL;DR

This paper extends the Event Calculus with probabilistic reasoning using Markov Logic Networks to better handle uncertainty in event recognition, demonstrated through activity recognition experiments on surveillance data.

Contribution

It introduces a probabilistic extension of the Event Calculus that addresses temporal semantics challenges and modifies inertia properties, enhancing event recognition under uncertainty.

Findings

Probabilistic Event Calculus improves event recognition accuracy.

The model effectively handles uncertainty in temporal reasoning.

Experimental results show advantages over traditional methods.

Abstract

Symbolic event recognition systems have been successfully applied to a variety of application domains, extracting useful information in the form of events, allowing experts or other systems to monitor and respond when significant events are recognised. In a typical event recognition application, however, these systems often have to deal with a significant amount of uncertainty. In this paper, we address the issue of uncertainty in logic-based 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 the 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 study how probabilistic modelling changes the behaviour of the formalism, affecting its key property, the inertia of fluents. Furthermore, we demonstrate the advantages of the probabilistic Event Calculus through examples and experiments in the domain of activity recognition, using a publicly available dataset for video surveillance.