A Hybrid Neuro-Symbolic Approach for Complex Event Processing

Abstract 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.
  • Marc Roig Vilamala (Cardiff)
  • Harrison Taylor (Cardiff)
  • Tianwei Xing (UCLA)
  • Luis Garcia (UCLA)
  • Mani Srivastava (UCLA)
  • Lance Kaplan (ARL)
  • Alun Preece (Cardiff)
  • Angelika Kimmig (Cardiff)
  • Federico Cerutti
Date Oct-2020