First International Workshop on Artificial Intelligence for Autonomous computing Systems (AI4AS 2023)

Co-located with ACSOS 2023, which takes place in Toronto (Canada) - September 25-29, 2023.

Important Dates

  • Submission deadline: July 10th, 2023 (firm)
  • Notification to authors: July 30th, 2023
  • Camera-ready deadline: August 5th, 2023
  • Workshop: September 28th, 2023 (Program)

All times in Anywhere on Earth (AoE) timezone.

Call for Papers

Modern computing systems are large and heterogeneous. Their complexity is hardly manageable by a human being, especially when it comes to taking timely decisions in highly dynamic environments or to guarantee strict Quality-of-Service requirements. Not surprisingly, recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) significantly impacted and fostered the development of autonomous computing systems, providing new or enhanced methodologies to cope with system complexity and uncertainty. AI and ML techniques are increasingly adopted to assist or guide system self-adaptation, as they are used, e.g., to extract relevant information from highly dimensional and noisy monitoring data, to predict internal or external dynamics, to automatically plan adaptation actions.

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.

We invite submissions of original research papers, as well as vision papers and experience reports.


The aim of the workshop is to share new findings, exchange ideas and discuss research challenges on the following topics (not an exhaustive list):

  • AI and ML techniques for self-* computing systems
  • Architectures and frameworks for AI integration
  • Sustainability aspects of AI-driven adaptation
  • Federated and multi-agent learning approaches for decentralized adaptation
  • Robustness, explainability, safety, and security of AI-driven computing systems
  • Case studies and real-world implementations of AI for autonomous computing systems


Gabriele Russo Russo

Valeria Cardellini

Stefano Iannucci

Program Committee

Sherif Abdelwahed

Danilo Ardagna

Giuliano Casale

Emiliano Casalicchio

Janick Edinger

Omid Gheibi

Nagarajan Kandasamy

Raffaela Mirandola

Alexandre da Silva Veith


Room: B2-1 (more info about the venue here)


15.30-15.35 Opening
15.35-16.30 Keynote: Sherif Abdelwahed, “A Journey into Integrating Machine Learning and Model-Based Techniques for CPS Autonomy”
16.35-17.00 Sharmin Jahan, Sarra Alqahtani, Rose Gamble and Masrufa Bayesh. Automated Extraction of Security Profile Information from XAI Outcomes
17.00-17.25 Glaucia Melo, Nathalia Nascimento, Paulo Alencar and Donald Cowan. Variability-Aware Architecture for Human-Chatbot Interactions: Taming Levels of Automation
17.25-17.50 Anthony Baietto, Christopher Stewart and Trevor J. Bihl. Dataset Augmentation for Robust Spiking Neural Networks
17.50-18.00 Closing


A Journey into Integrating Machine Learning and Model-Based Techniques for CPS Autonomy

The realm of cyber-physical systems (CPS) is rapidly evolving, with increasing demands for systems that possess not only automation and intelligence but also the capability to autonomously manage themselves in dynamic environments. This presentation delves into the synergistic integration of machine learning (ML) and model-based techniques as a novel approach to address the complexities of designing self-managing CPS. This presentation explores the fusion of machine learning (ML) and model-based techniques in the design of self-managing cyber-physical systems (CPS). By combining domain knowledge and data-driven insights, this integrated approach enhances CPS autonomy, adaptability, and decision-making. The talk covers real-world applications, benefits, challenges, and strategies for achieving synergy between ML and model-based methods. Attendees will gain insights into the evolving landscape of CPS design, learning how to create more sophisticated and self-managing systems through this collaborative paradigm.


Sherif Abdelwahed is a Professor of Electrical and Computer Engineering (ECE) at Virginia Commonwealth University (VCU), where he teaches and conducts research in the area of computer engineering, with specific interests in autonomic computing, cyber-physical systems, formal verification and cyber-security. Before joining VCU in August 2017, he served as the associate director of the Distributed Analytics and Security Institute at Mississippi State University (MSU). He was also is also an Associate Professor in the ECE Department at MSU. He received his Ph.D in 2002 from the Department of Electrical and Computer Engineering at the University of Toronto. Throughout his academic tenure, Dr. Abdelwahed pioneered work on model-based design of autonomic computing systems and self-managing systems using control-theoretic techniques and model-integrated computing concepts. His research interests also include Cyber security and model-based self-protection, design and analysis of cyber-physical systems, fault diagnosis, modeling and analysis of discrete-event and hybrid systems, and formal verification. Dr. Abdelwahed has chaired several international conferences and conference tracks, and has served as technical committee member at various national and international conferences. He received the StatePride Faculty award for 2010 and 2011, the Bagley College of Engineering Hearin Faculty Excellence award in 2010, and the 2016 Faculty Research Award from the Bagley College of Engineering at MSU. He has more than 180 publications and is a senior member of the IEEE.

Author Information

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 and must be no longer than 6 pages (including figures, tables, and references).

Note: when submitting via EasyChair, make sure that the track indicated as Workshop on Artificial Intelligence for Autonomous computing Systems is selected.

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.