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Predicate Calculus for Perception-led Automata
Byrne, Thomas J.
Byrne, Thomas J.
Publication Date
2023
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The University of Bradford theses are licenced under a Creative Commons Licence.
Peer-Reviewed
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Accepted for publication
Institution
University of Bradford
Department
School of Engineering. Faculty of Engineering and Digital Technologies
Awarded
2023
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Abstract
Artificial Intelligence is a fuzzy concept. My role, as I see it, is to put
down a working definition, a criterion, and a set of assumptions to set
up equations for a workable methodology. This research introduces the
notion of Artificial Intelligent Agency, denoting the application of Artificial
General Intelligence. The problem being handled by mathematics and
logic, and only thereafter semantics, is Self-Supervised Machine Learning
(SSML) towards Intuitive Vehicle Health Management, in the domain of
cybernetic-physical science.
The present work stems from a broader engagement with a major multinational
automotive OEM, where Intelligent Vehicle Health Management
will dynamically choose suitable variants only to realise predefined variation
points. Physics-based models infer properties of a model of the system,
not properties of the implemented system itself. The validity of their
inference depends on the models’ degree of fidelity, which is always an approximate
localised engineering abstraction. In sum, people are not very
good at establishing causality.
To deduce new truths from implicit patterns in the data about the physical
processes that generate the data, the kernel of this transformative technology
is the intersystem architecture, occurring in-between and involving the physical and engineered system and the construct thereof, through the communication core at their interface. In this thesis it is shown that the
most practicable way to establish causality is by transforming application models into actual implementation. The hypothesis being that the ideal source of training data for SSML, is an isomorphic monoid of indexical facts, trace-preserving events of natural kind.
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Type
Thesis
Qualification name
PhD