3rd International Workshop on Artificial Intelligence for Autonomous computing Systems (AI4AS 2025)

Co-located with ACSOS 2025, which takes place in Tokyo (Japan) - Mon 29 September - Fri 3 October 2025.

Important Dates

  • Submission deadline: June 29th, 2025
  • Notification to authors: July 24th, 2025
  • Camera-ready deadline: July 31st, 2025
  • Workshop: TBA

All times in Anywhere on Earth (AoE) timezone.

Call for Papers

Modern computing systems are characterized by increasing heterogeneity and operate on larger and larger scales. 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. With the rapid evolution of AI and ML techniques - including generative AI, agentic AI, and edge intelligence - new opportunities have emerged for designing more robust, sustainable, and secure computing systems. 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 (and possibly activate) 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.

Topics

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
  • AI ethics, bias mitigation, and trustworthiness in self-adaptive systems
  • Federated and multi-agent learning approaches for decentralized adaptation
  • Robustness, explainability, safety, and security of AI-driven computing systems
  • Integration of large language models (LLMs) and generative AI into autonomous computing systems
  • Edge intelligence and distributed decision-making in autonomous systems
  • Self-adaptation for AI/ML systems
  • Case studies and real-world implementations of AI for autonomous computing systems

Organizers

Valeria Cardellini

Ilias Gerostathopoulos

Stefano Iannucci

Gabriele Russo Russo

Program Committee

Sherif Abdelwahed

Jesse Ables

Ivana Dusparic

David Garlan

Sona Ghahremani

Ann Gentile

Emilio Incerto

Jialong Li

Matteo Nardelli

Raffaela Mirandola

Sara Pederzoli

Gregor Schiele

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, 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.

Previous Editions