Organic Computing postulates to equip technical systems with ‘lifelike’ properties. Technically, this means to move traditional design‑time decisions to runtime and into the responsibility of systems themselves. As a result, systems have a dramatically increased decision freedom that leads to highly autonomous behaviour. The goal of this process is to allow for self‑adaptation and self‑improvement of system behaviour at runtime. Especially since conditions that occur at runtime can only be anticipated to a certain degree, efficient mechanisms are needed that guide the system’s behaviour even in cases of missing knowledge or uncertain environmental states. Consequently, the research field of Organic Computing investigates fundamental principles, discusses essential aspects, and researches novel methods that are needed to finally build self‑adaptive and self‑organising systems that are capable of reliable operation in complex real‑world environments.

The ARCS conferences series has over 36 years of tradition reporting leading edge research in computer architecture and operating systems. The focus of the 2025 conference will be on mastering novel HPC chip architectures. Within the scope of the ARCS main conference, the special track on `Organic Computing’ focusses on developments and open challenges in the field of designing self-adaptive and self-organising systems embedded in real-world environments.

Contributions will be part of the conference proceedings. The proceedings of ARCS 2025 will be published in the Springer Lecture Notes on Computer Science (LNCS) series. The conference will be a physical meeting with a social program in Kiel, Germany.

... are on a wide range of aspects of `Organic Computing', including (but not limited to):

• A: Principles of OC Systems
  • System and Agent Architectures
  • Systems Engineering
  • Design Techniques and Processes
  • Nature-Inspiration to Deal with System Complexity
  • Self-x Properties and Lifelike Qualities
  • Self-Organisation Schemes for Highly Decentralised Systems

• B: Autonomous Learning Behaviour in Technical Systems
  • Reinforcement Learning from Interaction
  • Active Learning
  • Transfer Learning
  • Online Concept Drift/Shift and Novelty/Obsoleteness Detection
  • Transductive Inference for Efficient Model Building
  • Self-Awareness and Self-Reflection

• C: Self-Adaptation@Runtime
  • Adaptive Monitoring and Control
  • Mechanisms to deal with Continual Change
  • Robustness and Flexibility
  • Automated Algorithm (Re-)Configuration & Selection
  • Context-awareness and Transient Interfaces

• D: Metrics and Quantification
  • System Validation
  • Verification
  • Understanding and Explanation
  • Computational Trust
  • Testbeds and Performance Analysis

• E: Hardware Solutions
  • Adaptive Hardware
  • Reconfigurable Hardware
  • Embedded AI
  • Self-Adaptive, Self-Optimising, Self-Healing Hardware
  • Evolvable Hardware
  • Prototypes and Demonstrators

• F: Applications of OC Technology
  • Novel Use Cases
  • Case Study Reports
  • Experience Reports

Submissions should be done through EasyChair by selecting the Organic Computing track. Papers must be submitted in PDF format.

Contributions must be formatted according to Springer LNCS style and must not exceed 15 pages, including references and figures.

Anthony Stein, Universität Hohenheim (anthony.stein@uni-hohenheim.de)

Stefan Wildermann, Friedrich-Alexander Universität Erlangen (stefan.wildermann@fau.de)