  Systems for symbolic event recognition infer occurrences of events in time
using a set of event definitions in the form of first-order rules. The Event
Calculus is a temporal logic that has been used as a basis in event recognition
applications, providing among others, direct connections to machine learning,
via Inductive Logic Programming (ILP). We present an ILP system for online
learning of Event Calculus theories. To allow for a single-pass learning
strategy, we use the Hoeffding bound for evaluating clauses on a subset of the
input stream. We employ a decoupling scheme of the Event Calculus axioms during
the learning process, that allows to learn each clause in isolation. Moreover,
we use abductive-inductive logic programming techniques to handle unobserved
target predicates. We evaluate our approach on an activity recognition
application and compare it to a number of batch learning techniques. We obtain
results of comparable predicative accuracy with significant speed-ups in
training time. We also outperform hand-crafted rules and match the performance
of a sound incremental learner that can only operate on noise-free datasets.
This paper is under consideration for acceptance in TPLP.
