The application of knowledge based systems to the abstraction of design and costing rules in bespoke pipe jointing systems

This thesis presents the work undertaken in the creation of a knowledge based system aimed at facilitating the design and cost estimation of bespoke pipe jointing systems. An overview of the problem domain is provided and the findings from a literature review on knowledge based systems and applications in manufacturing were used to provide initial guidance to the research. The overall investigation and development process involved the abstraction of design and costing rules from domain experts using a sub-set of the techniques reviewed and the development and implementation of the knowledge based system using an expert system approach, the soft systems methodology (SSM) and the system development lifecycle methodology. Based on the abstracted design and costing rules, the developed system automates the design of pipe jointing systems, and facilitates cost estimation process within third party configuration software. The developed system was validated using two case studies and was shown to provide the required outputs

Informed selection and use of training examples for knowledge refinement.

Knowledge refinement tools seek to correct faulty rule-based systems by identifying and repairing faults indicated by training examples that provide evidence of faults. This thesis proposes mechanisms that improve the effectiveness and efficiency of refinement tools by the best use and selection of training examples. The refinement task is sufficiently complex that the space of possible refinements demands a heuristic search. Refinement tools typically use hill-climbing search to identify suitable repairs but run the risk of getting caught in local optima. A novel contribution of this thesis is solving the local optima problem by converting the hill-climbing search into a best-first search that can backtrack to previous refinement states. The thesis explores how different backtracking heuristics and training example ordering heuristics affect refinement effectiveness and efficiency. Refinement tools rely on a representative set of training examples to identify faults and influence repair choices. In real environments it is often difficult to obtain a large set of training examples, since each problem-solving task must be labelled with the expert's solution. Another novel aspect introduced in this thesis is informed selection of examples for knowledge refinement, where suitable examples are selected from a set of unlabelled examples, so that only the subset requires to be labelled. Conversely, if a large set of labelled examples is available, it still makes sense to have mechanisms that can select a representative set of examples beneficial for the refinement task, thereby avoiding unnecessary example processing costs. Finally, an experimental evaluation of example utilisation and selection strategies on two artificial domains and one real application are presented. Informed backtracking is able to effectively deal with local optima by moving search to more promising areas, while informed ordering of training examples reduces search effort by ensuring that more pressing faults are dealt with early on in the search. Additionally, example selection methods achieve similar refinement accuracy with significantly fewer examples