Towards explainability in robotics: A performance analysis of a cloud accountability system. Issue 9 (25th April 2022)

• Record Type: Journal Article
• Authors: Rodríguez‐Lera, Francisco Javier; González‐Santamarta, Miguel Ángel; Guerrero‐Higueras, Ángel Manuel; Martín‐Rico, Francisco; Matellán‐Olivera, Vicente
• Other Names: Herrero Álvaro guestEditor.; Urda Daniel guestEditor.; Sedano Javier guestEditor.; Quintián Héctor guestEditor.; Corchado Emilio guestEditor.; Ahmed Syed Hassan guestEditor.; Khan Murad guestEditor.; Guibene Wael guestEditor.
• Is Part Of: Expert systems. Volume 39:Issue 9(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-04-25
• Subjects: accountability -- autonomous agents -- explainability -- traceability; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.13004
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: 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.