A large class of software-intensive systems, including those for industrial automation, consumer electronics, airplanes, automobiles, medical devices, and civic infrastructure, must interact with the physical world. More advanced systems, such as unmanned autonomous systems, don’t just interact but also perceive important structural and dynamic aspects of their operational environment. To become interactive, an autonomous system must be aware of its physical environment and whereabouts, as well as its current internal status. This ability helps software-intensive systems sense, draw inferences, and react by exhibiting self-adaptation. As software is used for more pervasive and critical applications, support for self-adaptation is increasingly seen as necessary in avoiding costly disruptions for repair, maintenance and evolution of systems. A common understanding about the process of self-adaptation is the ability of a system to autonomously monitor its behavior and eventually modify the same according to changes in the operational environment or in the system itself. A good example of self-adaptive systems can be addressed to contemporary robotics systems that rely on the most recent advances in automation and robotic technologies to promote autonomy and self-adaptation to robotized systems.
The paradigm of self-adaptive systems is closely related to AI, which makes the research and development of such systems extremely challenging and demanding new approaches that can efficiently tackle the problems of expressing autonomy requirements, designing and implementing self-adaptive features, and efficiently testing self-adaptive behavior.
This journal seeks contributions from leading experts from research and practice of self-adaptive systems that will provide the connection between theory and practice with the ultimate goal to bring both the science and industry closer to the so-called "autonomic culture” and successful realization of self-adaptive systems. Both theoretical and applied contributions related to the relevance and potential of engineering methods, approaches and tools for self-adaptive systems are particularly welcome. This applies to application areas and technologies such as:
- adaptable security and privacy;
- adaptable user interfaces;
- autonomic computing;
- dependable computing;
- embedded systems;
- genetic algorithms;
- knowledge representation and reasoning;
- machine learning;
- mobile ad-hoc networks;
- mobile and autonomous robots;
- multi-agent systems;
- peer-to-peer applications;
- sensor networks;
- service-oriented architectures;
- ubiquitous computing.
It also hold for many research fields, which have already investigated some aspects of self-adaptation from their own perspective, such as fault-tolerant computing, distributed systems, biologically inspired computing, distributed artificial intelligence, integrated management, robotics, knowledge-based systems, machine learning, control theory, etc.