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Título
Towards explainability in robotics: A performance analysis of a cloud accountability system
Autor
Facultad/Centro
Área de conocimiento
Título de la revista
Expert Systems
Número de la revista
9
Cita Bibliográfica
Rodríguez-Lera, F. J., González-Santamarta, M. Á., Guerrero-Higueras, Á. M., Martín-Rico, F., & Matellán-Olivera, V. (2022). Towards explainability in robotics: A performance analysis of a cloud accountability system. Expert Systems, 39(9). https://doi.org/10.1111/EXSY.13004
Editorial
Wiley-Blackwell
Fecha
2022
ISSN
0266-4720
Resumen
[EN] Understanding why a robot's behaviour was triggered is a growing concern to get human-acceptable social robots. Every action, expected and unexpected, should be able to be explained and audited. The formal model proposed here deals with different information levels, from low-level data, such as sensors' data logging; to high-level data that provide an explanation of the robot's behaviour. This study examines the impact on the robot system of a custom log engine based on a custom ROS logging node and investigates pros and cons when used together with a NoSQL database locally and in a cloud environment. Results allow to characterize these alternatives and explore the best strategy for offering a fully log-based accountability engine that maximizes the mapping between robot behaviour and robot logs.
Materia
Palabras clave
Peer review
SI
ID proyecto
- info:eu-repo/grantAgreement/AEI/Programa Estatal de I+D+i Orientada a los Retos de la Sociedad/RTI2018-100683-B-I00/ES/DETECCION Y CARACTERIZACION AUTOMATICA DE PROBLEMAS DE CIBERSEGURIDAD EN PLATAFORMAS ROBOTICAS
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DOI
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