The complexity of today’s and tomorrows’ computer-based/cyber-physical systems-of-systems, consisting of very large numbers of elements, must be increasingly autonomous in order to carry out their complex tasks without constant human input. To reach those increasing levels of autonomy, the elements themselves must have the capability to self-manage, that being; self-configure, self-heal, self-optimise and self-protect through the abilities of self-awareness, self-situation (context and environment-awareness), self-monitoring and self-adjusting. This self-management specialisation of Autonomy, is Autonomicity, or the field of Autonomic Computing. The cumulative contribution of this thesis is several-fold; adding to IBM’s Autonomic Computing initiative by specifying the self-* properties as an Autonomic Computing quality tree and redefining the Autonomic Element and Architecture with these self-* properties and tighter scoping engineering rules;. by engineering dynamics within autonomic responses and multiple loops of control, such as reflex reactions among the autonomic managers and architecture through “Pulse Monitoring” and the associated artifact Pulse Monitor (PBM) to provide that autonomic reflex reaction; engineering such developments to Autonomic Personal Computing; engineering such to NASA and their future (SWARM based) concept missions; engineering such to Autonomic Communications; ‘Inventing’ the field of Apoptotic Computing, inspired by biological cellular pre-programmed ‘self-destruct’ behaviour, as the ultimate security/trust mechanism for Autonomic Systems. All of these have added to the “vision” of Autonomic Computing, as assessed by IBM and the field’s top-cited researcher. This mix of Autonomic Computing, Autonomic Communications and Apoptotic Computing leads to a more general umbrella term of Autonomic Systems. Sterritt’s 20+ years of Autonomic Computing research with academia and industry has led to more ‘Engineering Self-Management into Computer-Based Systems’ than can be covered in this accumulative thesis, so much so that Autonomic-* becomes the norm with a hypothesis that ‘all Computer-Based Systems Should be Autonomic’. This view results in many future paths for this research, both in improving the paradigm itself and applying/engineering it to other fields. That said, the last 20+ years of Autonomic Systems (AS) has been mostly an (Software and Systems) Engineering accomplishment. As a field, its next level future growth may be part of the latest A.I. incarnation – Generative AI, as it evolves towards Artificial General Intelligence (AGI). Essentially, AS may be considered advanced automation. Gen-AI AS may provide accelerated AI automation in the next decade performing domain-bounded tasks that exhibit three fundamental characteristics: autonomy, learning, and agency and have an equivalent level of industrial success as Gen-AI.
Roy Sterritt (Wed,) studied this question.