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AI4AS 2024: Second International Workshop on
Artificial Intelligence for Autonomous computing Systems
Web: https://ai4as.github.io

September 16-20, 2024 - Aarhus, Denmark
co-located with ACSOS 2024 (https://2024.acsos.org)
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IMPORTANT DATES
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Manuscript submission: July 4th, 2024 (AoE), extended
Notification to authors: July 18th, 2024
Camera-ready: July 26th, 2024
Workshop: September 16th or 20th, 2024



SCOPE AND TOPICS
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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.

Authors of selected papers from the workshop will be invited to
submit an extended version of their work to the special issue of ACM
Transactions on Autonomous and Adaptive Systems (TAAS) on "Artificial
Intelligence for Adaptive and Autonomous Cloud/Edge 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
- Integration of large language models (LLMs) into autonomous computing systems
- Self-adaptation for AI/ML systems
- Case studies and real-world implementations of AI for autonomous computing systems

SUBMISSION INSTRUCTIONS
-----------------------
Papers can be submitted in PDF format via EasyChair and 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.

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

EasyChair link: https://easychair.org/conferences/submission_new?a=32711075


ORGANIZING COMMITTEE
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Workshop co-chairs
- Gabriele Russo Russo, Tor Vergata University of Rome, Italy
- Valeria Cardellini, Tor Vergata University of Rome, Italy
- Paolo Romano, University of Lisbon, Portugal

Technical program committee
- Sherif Abdelwahed, Virginia Commonwealth University, USA
- Ivana Dusparic, Trinity College Dublin, Ireland
- David Garlan, Carnegie Mellon University, USA
- Janick Edinger, University of Hamburg, Germany
- Shashikant Ilager, TU Wien, Austria
- Emilio Incerto, IMT School for Advanced Studies, Italy
- Matteo Nardelli, Bank of Italy, Italy
- Luis Rodrigues, University of Lisbon, Portugal
- Gregor Schiele, University of Duisburg-Essen, Germany