Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Probabilistic Event Calculus for Event Recognition   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.

19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex