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.
Cite
@article{arxiv.2009.03420,
title = {A Hybrid Neuro-Symbolic Approach for Complex Event Processing},
author = {Marc Roig Vilamala and Harrison Taylor and Tianwei Xing and Luis Garcia and Mani Srivastava and Lance Kaplan and Alun Preece and Angelika Kimmig and Federico Cerutti},
journal= {arXiv preprint arXiv:2009.03420},
year = {2020}
}
Comments
Accepted as extended abstract at ICLP2020