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dc.contributor.advisorNeagu, Daniel
dc.contributor.advisorCampean, I. Felician
dc.contributor.authorByrne, Thomas J.
dc.date.accessioned2024-06-04T11:27:36Z
dc.date.available2024-06-04T11:27:36Z
dc.date.issued2023
dc.identifier.urihttp://hdl.handle.net/10454/19905
dc.description.abstractArtificial 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.en_US
dc.language.isoenen_US
dc.rights<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>.eng
dc.subjectArtificial intelligence (AI)en_US
dc.subjectArtificial General Intelligenceen_US
dc.subjectSelf-supervised machine learning (SSML)en_US
dc.subjectIntuitive vehicle health managementen_US
dc.subjectIntersystem architectureen_US
dc.subjectCyber-physical scienceen_US
dc.titlePredicate Calculus for Perception-led Automataen_US
dc.type.qualificationleveldoctoralen_US
dc.publisher.institutionUniversity of Bradfordeng
dc.publisher.departmentSchool of Engineering. Faculty of Engineering and Digital Technologiesen_US
dc.typeThesiseng
dc.type.qualificationnamePhDen_US
dc.date.awarded2023
refterms.dateFOA2024-06-04T11:27:36Z


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