The IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) is the premier venue for advancing research in autonomic computing, self-adaptation, and self-organization. The conference serves as a synergistic forum for interdisciplinary collaboration, bridging academic and industrial perspectives across domains such as artificial intelligence, computational biology, and computer systems. ACSOS features a diverse program, including research papers, experience reports, posters, demonstrations, and a doctoral symposium, fostering innovation and knowledge exchange.

ACSOS 2026 invites submissions on theoretical aspects, modeling, design, implementation, evaluation, verification, and practical applications of autonomic, self-adaptive, self-organizing, and multi-agent systems, algorithms, and techniques. This includes, but is not limited to, the following areas:

    Models and Algorithms: bio-inspired and socially inspired paradigms and heuristics; collective behavior of decentralized agents and systems; swarm intelligence; evolution and learning; organic computing; requirement and goal expression techniques; formal expressions of uncertainty; agent-based modeling to help understand existing systems; digital twins.
    Theoretical aspects: theoretical frameworks; formal languages; game theory; queuing and control theory; symbolic knowledge representation.
    Systems properties: performance; robustness; resilience, dependability, and reliability; trustworthiness; resource and energy efficiency; stability; diversity; self-protection and cybersecurity; self-reference and reflection; emergent behavior; explainability; interpretability; computational awareness and self-awareness.
    Engineering aspects: design patterns; programming languages; architectures; operating systems and middleware; testing, validation, and assurance methodologies; runtime models; large-scale, decentralized, and multi-agent systems; data science and analytics; machine learning and artificial intelligence; LLMs and agentic AI systems; communication and intelligent routing; distributed learning, including federated learning; multi-agent infrastructures.
    Cross-disciplinary methods: approaches inspired by complex systems, chemistry, psychology, sociology, biology, and ethology.
    Socio-technical factors: human and social factors; visualization; crowdsourcing and collective awareness; guardrails and legal or regulatory compliance in self-* techniques; trust, ethics, privacy, sustainability, and social and environmental implications.
    Applications: papers across all application domains are welcome, including (but not limited to) enterprise systems, cyber-physical systems, smart agriculture, robot swarms, federated LLMs, manufacturing, autonomous vehicles, smart environments, disaster response, smart traffic management, datacenter infrastructure, resource management and scheduling, smart grids, scientific computing, virtual reality, and human–machine interaction.