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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F24%3A00375788" target="_blank" >RIV/68407700:21730/24:00375788 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ITSC57777.2023.10422348" target="_blank" >http://dx.doi.org/10.1109/ITSC57777.2023.10422348</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ITSC57777.2023.10422348" target="_blank" >10.1109/ITSC57777.2023.10422348</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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 realworld 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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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 realworld 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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA23-05575S" target="_blank" >GA23-05575S: Řízení dopravy v obydlených oblastech pomocí automatického plánování</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

ISBN

979-8-3503-9946-2

ISSN

2153-0017

e-ISSN

2153-0017

Počet stran výsledku

6

Strana od-do

4913-4918

Název nakladatele

IEEE (Institute of Electrical and Electronics Engineers)

Místo vydání

—

Místo konání akce

Bilbao

Datum konání akce

24. 9. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001178996704141