Self Learning and Self Adaptive Systems
(SL-SAS)

Belgrade, Serbia, 8–11 September, 2024

Self-learning systems are artificial agents able to acquire and renew knowledge over time without hard coding. These are adaptive systems whose functions improve by a learning process based -typically- on the method of trial and error. A self-learning system initially interacts with its users or surrounding environment by attempting and observing the changes produced by its actions. This track will focus on the design, implementation and exploitation of self-learning features within an Intelligent Environment or some of its components. The thematic track will represent an opportunity for academia and industry to debate the state-of-the-art challenges and open issues.

Topics

Artificial Intelligence
Reinforcement Learning
Inverse Reinforcement Learning
Safe Reinforcement Learning
Deep Learning
Ambient Intelligence
Federated Learning

Thematic Track organizers

Coronato, Antonio, Università Giustino Fortunato, Benevento, Italy
Naeem, Muddasar, Università Giustino Fortunato, Benevento, Italy

Contact: sl-sas@fedcsis.org

Submission rules

Authors should submit their papers as Postscript, PDF or MSWord files.
The total length of a paper should not exceed 12 pages IEEE style (including tables, figures and references). More pages can be added, for an additional fee. IEEE style templates are available here.
Papers will be refereed and accepted on the basis of their scientific merit and relevance to the Topical Area.
Preprints containing accepted papers will be published online.
Only papers presented at the conference will be published in Conference Proceedings and submitted for inclusion in the IEEE Xplore® database.
Conference proceedings will be published in a volume with ISBN, ISSN and DOI numbers and posted at the conference WWW site.
Conference proceedings will be submitted for indexation according to information here.
Organizers reserve right to move accepted papers between FedCSIS Tracks.