Paper Info
Title
Applying Neural-Symbolic Cognitive Agents In Intelligent Transport Systems To Reduce Co2 Emissions
Abstract
Providing personalized feedback in Intelligent Transport Systems is a powerful tool for instigating a change in driving behaviour and the reduction of CO2 emissions. This requires a system that is capable of detecting driver characteristics from real-time vehicle data. In this paper, we apply the architecture and theory of a Neural-Symbolic Cognitive Agent (NSCA) to effectively learn and reason about observed driving behaviour and related driver characteristics. The NSCA architecture combines neural learning and reasoning with symbolic temporal knowledge representation and is capable of encoding background knowledge, learning new hypotheses from observed data, and inferring new beliefs based on these hypotheses. Furthermore, it deals with uncertainty and errors in the data using a Bayesian inference model, and it scales well to hundreds of thousands of data samples as in the application reported in this paper. We have applied the NSCA in an Intelligent Transport System to reduce CO2 emissions as part of an European Union project, called EcoDriver. Results reported in this paper show that the NSCA outperforms the state-of-the-art in this application area, and is applicable to very large data.
Year
Venue
Keywords
2014
PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
Neural-Symbolic Learning and Reasoning, Driver modelling, Deep Learning, Restricted Boltzmann Machines (RBM)
DocType
ISSN
Citations 
Conference
2161-4393
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Leo de Penning163.19
Artur S. D'avila Garcez243163.57
Luís C. Lamb328050.02
Arjan Stuiver431.12
John-Jules Ch. Meyer52316286.04