Dynamic Architectures for Computational Self-Awareness

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There has been growing interest in recent years regarding the exploration of technical systems andhow they can express autonomous behaviour as a way of addressing a wide range of challenges;modern systems are being used in increasingly diverse, unpredictable and dynamic environments,creating a need for systems to overcome unknown states and situations through reasoning andlearning - without human intervention. As the environments and the goals of the systems are morecommonly subject to change over time, it is becoming impossible for programmers to know the"correct" approach to solving run-time problems whilst designing the systems themselves; it istherefore increasingly important for systems to have the ability to autonomously adapt to respondto these changes in an effective and efficient manner. One such approach to tackling these issues is designing systems with computational self-awareness capabilities, so they can learn and act withoutany prior knowledge, whilst extending their skills over time.With this in mind, this project therefore aims to look at how self-aware computing systems can bedesigned to operate in an ever-changing environment, specifically looking at the additionalcomplexity of interactions between systems and how these can be engineered to be efficient andmeaningful. Further, the focus is on system to system interactions, rather than the larger area ofsociotechnical systems where there are complex interactions between systems and humans alike.One question this project aims to address surrounds how systems are able to learn and achieve theirown goals in an environment where their own actions are affected by another - either implicitly or explicitly. Researchers have asked questions about issues of integration which considers explicitissues in the design of the system itself; this project however aims to investigate the unintendedinteractions that may arise between two interacting systems, and how systems can be designed tocombat these issues autonomously. This involves consideration of how one models both itself andothers, and how these so-called "social systems" are able to develop a deeper understanding of their place in the world around them, as well as the impact others can have on themselves. Theories of human psychology and sociology will be used as inspiration to answer these questions.A further question that arises from this regards the learning processes themselves that a system can exhibit, and how these relate to the context systems operate in. For example, the outcome of a certain behaviour can change depending on the context/situation; if the system is not complex enough to perceive the cause of this change (i.e. the actions of another, a change in the environment), it will appear to the system that there is no reason for this change, resulting in the system changing its knowledge accordingly. A consequence of this is that well-learnt and successful knowledge and behaviour may be changed or lost due to unknown interactions. This project therefore also aims to investigate whether the learning processes of systems can be modularised, such that contextual change does not interfere with learnt knowledge. This way, systems may be able to interact more effectively due to a more robust learning approach.