A Hybrid Neuro-Symbolic Approach for Complex Event Processing
  Training a model to detect patterns of interrelated events that form
situations of interest can be a complex problem: such situations tend to be
uncommon, and only sparse data is available. We propose a hybrid neuro-symbolic
architecture based on Event Calculus that can perform Complex Event Processing
(CEP). It leverages both a neural network to interpret inputs and logical rules
that express the pattern of the complex event. Our approach is capable of
training with much fewer labelled data than a pure neural network approach, and
to learn to classify individual events even when training in an end-to-end
manner. We demonstrate this comparing our approach against a pure neural
network approach on a dataset based on Urban Sounds 8K.
