  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.
