  Complex Event Recognition (CER) systems detect event occurrences in streaming
time-stamped input using predefined event patterns. Logic-based approaches are
of special interest in CER, since, via Statistical Relational AI, they combine
uncertainty-resilient reasoning with time and change, with machine learning,
thus alleviating the cost of manual event pattern authoring. We present a
system based on Answer Set Programming (ASP), capable of probabilistic
reasoning with complex event patterns in the form of weighted rules in the
Event Calculus, whose structure and weights are learnt online. We compare our
ASP-based implementation with a Markov Logic-based one and with a number of
state-of-the-art batch learning algorithms on CER datasets for activity
recognition, maritime surveillance and fleet management. Our results
demonstrate the superiority of our novel approach, both in terms of efficiency
and predictive performance. This paper is under consideration for publication
in Theory and Practice of Logic Programming (TPLP).
