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Title: Formal methods of argumentation as models of engineering design decisions and processes
Author: Mueller, J. F.
ISNI:       0000 0004 8498 7112
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2017
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Complex engineering projects comprise many individual design decisions. As these decisions are made over the course of months, even years, and across different teams of engineers, it is common for them to be based on different, possibly conflicting assumptions. The longer these inconsistencies go undetected, the costlier they are to resolve. Therefore it is important to spot them as early as possible. There is currently no software aimed explicitly at detecting inconsistencies in interrelated design decisions. This thesis is a step towards the development of such tools. We use formal methods of argumentation, a branch of artificial intelligence, as the foundation of a logical model of design decisions capable of handling inconsistency. It has three parts. First, argumentation is used to model the pros and cons of individual decisions and to reason about the possible worlds in which these arguments are justified. In the second part we study sequences of interrelated decisions. We identify cases where the arguments in one decision invalidate the justification for another decision, and develop a measure of the impact that choosing a specific option has on the consistency of the overall design. The final part of the thesis is concerned with non-deductive arguments, which are used in design debates, for example to draw analogies between past and current problems. Our model integrates deductive and non-deductive arguments side-by-side. This work is supported by our collaboration with the engineering department of Queen's University Belfast and an industrial partner. The thesis contains two case studies of realistic problems and parts of it were implemented as software prototypes. We also give theoretical results demonstrating the internal consistency of our model.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available