Faster Than LASER - Towards Stream Reasoning with Deep Neural Networks

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With the constant increase of available data in various domains, such as the Internet of Things, Social Networks or Smart Cities, it has become fundamental that agents are able to process and reason with such data in real time. Whereas reasoning over time-annotated data with background knowledge may be challenging, due to the volume and velocity in which such data is being produced, such complex reasoning is necessary in scenarios where agents need to discover potential problems and this cannot be done with simple stream processing techniques. Stream Reasoners aim at bridging this gap between reasoning and stream processing and LASER is such a stream reasoner designed to analyse and perform complex reasoning over streams of data. It is based on LARS, a rule-based logical language extending Answer Set Programming, and it has shown better runtime results than other state-of-the-art stream reasoning systems. Nevertheless, for high levels of data throughput even LASER may be unable to compute answers in a timely fashion. In this paper, we study whether Convolutional and Recurrent Neural Networks, which have shown to be particularly well-suited for time series forecasting and classification, can be trained to approximate reasoning with LASER, so that agents can benefit from their high processing speed.

Original languageEnglish
Title of host publicationProgress in Artificial Intelligence - 20th EPIA Conference on Artificial Intelligence, EPIA 2021, Proceedings
EditorsGoreti Marreiros, Francisco S. Melo, Nuno Lau, Henrique Lopes Cardoso, Luís Paulo Reis
Place of PublicationCham
Number of pages13
ISBN (Electronic)978-3-030-86230-5
ISBN (Print)978-3-030-86229-9
Publication statusPublished - 2021
Event20th EPIA Conference on Artificial Intelligence, EPIA 2021 - Virtual, Online
Duration: 7 Sept 20219 Sept 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12981 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference20th EPIA Conference on Artificial Intelligence, EPIA 2021
CityVirtual, Online


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