In Defence of Good Old-Fashioned Artificial Intelligence Approaches in Intelligent Transportation Systems

Mauro Vallati, Lukáš Chrpa

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

In recent years, Artificial Intelligence (AI) has been increasingly used in traffic management and control, particularly in the smart city context. However, the vast majority of recent AI-based approaches rely on data-driven black-box models that hinder the ability to understand the behaviour and dynamics that lead to a given output. On the contrary, Good Old-Fashioned Artificial Intelligence approaches that are based on symbolic models, such as automated planning, can provide the transparency and explainability needed in real world applications.

This paper focuses on the benefits of using automated planning techniques in Intelligent Transportation Systems (ITS), with a focus on explainability. A case study is presented to demonstrate how the components of an automated planning system can support explainability, the types of explanations that can be obtained, and the way in which such explanations can be generated.
Original languageEnglish
Title of host publicationProceedings of the 26th IEEE International Conference on Intelligent Transportation Systems
Subtitle of host publicationITSC 2023
PublisherIEEE
Pages4913-4918
Number of pages6
ISBN (Electronic)9798350399462
ISBN (Print)9798350399479
DOIs
Publication statusPublished - 13 Feb 2024
Event26th IEEE International Conference on Intelligent Transportation Systems - Bilbao, Spain
Duration: 24 Sep 202328 Sep 2023
Conference number: 26
https://2023.ieee-itsc.org/

Publication series

NameInternational Conference on Intelligent Transportation Systems (ITSC)
PublisherIEEE
ISSN (Print)2153-0009
ISSN (Electronic)2153-0017

Conference

Conference26th IEEE International Conference on Intelligent Transportation Systems
Abbreviated titleITSC 2023
Country/TerritorySpain
CityBilbao
Period24/09/2328/09/23
Internet address

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