Publication
Neural-Symbolic Learning and Reasoning
Tarek R. Besold; Artur d'Avila Garcez; Ernesto Jimenez-Ruiz; Roberto Confalonieri; Pranava Madhyastha; Benedikt Wagner (Hrsg.)
International Conference on Neural-Symbolic Learning and Reasoning (NeSy-2024), September 9-12, Barcelona, Spain, ISBN 978-3-031-71170-1, Springer, 2024.
Abstract
Classifying parts of time series is an important task when it
comes to the usage of Artificial Neural Networks (ANN), e.g. for analyzing
the power consumption of households. To make it possible to adapt
such ANN for Non Intrusive Load Monitoring (NILM) for the household
in which they are deployed is crucial but not easy to manage. The Neurosymbolic
Artificial Intelligence (AI) approach in this paper makes it
possible to do that by combining ANN modules with Probabilistic Logic
which is used as a supervise process to check the outputs of the ANN
in case of plausibility. This on the one hand filters implausible results
out which is helpful for productive usage, on the other hand these post
processed results can be used to retrain the network and adapt it for a
specific household in a continual learning process.