4th International Workshop on Artificial Intelligence for Autonomous computing Systems (AI4AS 2026)
Co-located with ACSOS 2026, which takes place in Cesena (Italy) - September 7-11, 2026.
Co-located with ACSOS 2026, which takes place in Cesena (Italy) - September 7-11, 2026.
All times in Anywhere on Earth (AoE) timezone.
Modern computing systems are increasingly heterogeneous and operate at unprecedented scale across the cloud–edge continuum. Their structural and operational complexity often exceeds what can be effectively managed through manual configuration or static control logic, particularly when timely decisions are required in highly dynamic environments and strict Quality-of-Service (QoS) guarantees must be enforced.
The rapid evolution of Artificial Intelligence (AI) and Machine Learning (ML) techniques – including generative AI, agentic AI, edge intelligence, as well as collaborative and federated learning – has opened new avenues for engineering more robust, sustainable, and secure computing infrastructures. AI/ML techniques are increasingly embedded into the control loops of modern systems to enable self-adaptation. They are leveraged, for example, to extract actionable insights from high-dimensional and noisy monitoring data, to learn performance and workload models, to forecast internal or external dynamics (e.g., workload fluctuations or network conditions), and to automatically plan—and potentially enact—adaptation and resource management actions. Moreover, to scale this self-adaptive intelligence, collaborative learning paradigms enable multiple nodes or administrative domains to jointly build these predictive or prescriptive models by sharing knowledge rather than raw data, which is essential in large-scale distributed edge and cloud settings.
However, there are still several challenges to face for researchers and practitioners aiming to take advantage of these methodologies and incorporate them in their systems. Fundamental issues towards the applicability of AI and ML techniques across diverse domains must be investigated, especially as regards the accuracy, robustness, explainability, safety, security, performance and sustainability of AI-driven autonomous computing systems.
In this workshop, we solicit high-quality contributions that fit with the overarching theme of AI and ML meeting autonomous computing systems.
The aim of the workshop is to share new findings, exchange ideas and discuss research challenges on the following topics (not an exhaustive list):
All submissions are required to be formatted according to the standard IEEE Computer Society Press proceedings style guide. Papers can be submitted in PDF format via EasyChair, making sure to select the track “AI4AS-Workshop”. Submitted manuscripts must be no longer than 6 pages (including figures, tables, and references).
Accepted papers will be published in the ACSOS Companion volume and will appear in IEEE Xplore.
As per the standard IEEE policies, all submissions should be original, i.e., they should not have been previously published in any conference proceedings, book, or journal and should not currently be under review for another archival conference. We would like to also highlight IEEE’s policies regarding plagiarism and self-plagiarism, available here.
Moreover, as per IEEE guidelines, the use of content generated by artificial intelligence (AI) in a submission (including but not limited to text, figures, images, and code) shall be disclosed in the acknowledgments section.