Despite the active and proficuous research in autonomous and self-adaptive systems of the last years, an evaluation framework to assess abilities related to adaption and to provide guidance to make a system smarter is still missing. In this paper, we perform the first steps towards an evaluation framework for autonomous systems to (i) make an assessment of a system from the perspective of its capacity to adapt and learn over time to handle new and unexpected conditions, (ii) explore and identify the possible pathways of improvement of the smart abilities (e.g., decisional autonomy, adaptability, perception, interaction, etc.) of a system, (iii) make a re-assessment when the improvement has been performed.
Towards an Evaluation Framework for Autonomous Systems / A. Bombarda, S. Bonfanti, M. De Sanctis, A. Gargantini, P. Pelliccionet, E. Riccobene, P. Scandurra - In: 2022 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)[s.l] : IEEE, 2022. - ISBN 978-1-6654-5142-0. - pp. 43-48 (( Intervento presentato al 3. convegno IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion, ACSOS-C 2022 tenutosi a Virtual Event nel 2022 [10.1109/ACSOSC56246.2022.00025].
Towards an Evaluation Framework for Autonomous Systems
E. Riccobene
Penultimo
;
2022
Abstract
Despite the active and proficuous research in autonomous and self-adaptive systems of the last years, an evaluation framework to assess abilities related to adaption and to provide guidance to make a system smarter is still missing. In this paper, we perform the first steps towards an evaluation framework for autonomous systems to (i) make an assessment of a system from the perspective of its capacity to adapt and learn over time to handle new and unexpected conditions, (ii) explore and identify the possible pathways of improvement of the smart abilities (e.g., decisional autonomy, adaptability, perception, interaction, etc.) of a system, (iii) make a re-assessment when the improvement has been performed.
| File | Dimensione | Formato | |
|---|---|---|---|
|
ACSOS2022___MAR___MVM.pdf
accesso riservato
Tipologia:
Post-print, accepted manuscript ecc. (versione accettata dall'editore)
Dimensione
130.21 kB
Formato
Adobe PDF
|
130.21 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
|
Towards_an_Evaluation_Framework_for_Autonomous_Systems.pdf
accesso riservato
Tipologia:
Publisher's version/PDF
Dimensione
127.14 kB
Formato
Adobe PDF
|
127.14 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
