ECAS 2021

Modern computing systems tend to be composed of many distributed and heterogeneous entities interacting with one another and with their environment to pursue a variety of goals and functionality. These systems typically operate under continuous perturbations, making manual adjustments and open-loop approaches infeasible, hence requiring self-* features (e.g., self-organisation, self-adaptation, self-configuration...). For a collective system to be resilient, its adaptation must also be collective, in the sense that multiple entities must adapt in a way that addresses critical runtime conditions while preserving the benefits of collaborative interdependencies. Decision-making in such systems is distributed and possibly highly dispersed, and interaction between the entities may lead to the emergence of unexpected phenomena.

To engineer such collective adaptive systems (CAS), new approaches for and understanding of collective adaptation are needed, to allow: i) multiple entities to adapt in a coordinated or complementary way, with ii) negotiations or other mechanisms to decide which collective changes are suitable. Collective adaptation also raises a second important challenge: Which parts of the system (things, services, people) should be engaged in an adaptation, and how? This is nontrivial, as multiple solutions to the same problem may be generated at different levels, and individuals in the collective often have partial information. The challenge is to understand these levels and create mechanisms to decide the right scope for an adaptation for a given problem. 

This workshop solicits papers that address new methodologies, theories, principles, and fundamental understanding, that can be used to underpin the design, operation, and analysis of CASs. Case studies, applications showing such approaches in action, and interdisciplinary work are particularly welcome. Research on CAS engineering can benefit from advances in related areas looking “beyond individual devices”, including (but not limited to) multi-agent systems, coordination, concurrency theory, self-* systems, collective intelligence, nature-inspired computing, organisational paradigms, and so on.

• Novel theories relating to operating principles of CAS
• Novel design principles for building CAS systems
• Insights into the short and long-term adaptation of CAS systems
• Insights into emergent properties of CAS
• Insights into general properties of large scale, distributed CAS
• Comparing and analyzing approaches to CAS (e.g., distributed and centralized)
• Decision-making approaches in CAS
• Methodologies for studying, analyzing, and building CAS
• Frameworks for analyzing or developing CAS case studies
• Languages, platforms, APIs and other tools for CAS
• Scenarios, case studies, and experience reports of CAS in different contexts (e.g., Smart Mobility, Smart Energy/Smart Grid, Smart Buildings, traffic management, emergency response, etc.)