Lecture: Autonomous Systems – A Rigorous Architectural Characterization
The concept of autonomy is key to the IoT vision promising increasing integration of smart services and systems minimizing human intervention. This vision challenges our capability to build complex open trustworthy autonomous systems. We lack a rigorous common semantic framework for autonomous systems. There is currently a lot of confusion regarding the main characteristics of autonomous systems. In the literature, we find a profusion of poorly understood “self”-prefixed terms related to autonomy such as Self-healing, Self-optimization, Self-protection, Self-awareness, Self-organization etc. It is remarkable that the debate about autonomous vehicles focuses almost exclusively on AI and learning techniques while it ignores many other equally important autonomous system design issues.
Autonomous systems involve agents and objects coordinated in some common environment so that their collective behavior meets a set of global goals. We propose a general computational model combining a system architecture model and an agent model. The architecture model allows expression of dynamic reconfigurable multi-mode coordination between components. The agent model consists of five interacting modules implementing each one a characteristic feature: perception, reflection, goal management, planning and self-adaptation. It determines a concept of autonomic complexity accounting for the specific difficulty to build autonomous systems.
We emphasize that the main characteristic of autonomous systems is their ability to handle knowledge and adaptively respond to environment changes. A main conclusion is that autonomy should be associated with functionality and not with specific techniques.
We conclude that autonomy is a kind of broad intelligence. Building trustworthy and optimal autonomous systems goes far beyond the AI challenge.