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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/25329

Title: A self-learning case and rule-based reasoning algorithm for intelligent technology evaluation and selection [Abstract]
Authors: Evans, Liam
Lohse, Niels
Webb, Phil
Keywords: Case-based
Rule-based reasoning
Expert system
Technology selection
Experience-oriented problems
Self-learning
Issue Date: 2010
Citation: EVANS, L., LOHSE, N. and WEBB, P., 2010. A self-learning case and rule-based reasoning algorithm for intelligent technology evaluation and selection. Presented at the AI-2010 Thirtieth SGAI International Conference on Artificial Intelligence, Cambridge, UK, 14th-16th December.
Abstract: This research programme proposes to fulfill the existing gap in knowledge by providing an experience-oriented decision algorithm to solve technology selection problems based on cases and expert’s experience. The approach adopts historical case-based data to extract rules through the ID3 rule induction algorithm. The decision model integrates a rule induction approach in a rule-based knowledge system and database management system to support automated knowledge mining and usage. The adoption of a pair-wise comparison algorithm within the similarity index assists in relating the importance of the criteria within the knowledgebases reasoner. A series of historical and new solutions are presented in a scoring index based on the requirements of a new case.
Description: This is an abstract of a conference paper.
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/25329
Publisher Link: http://www.bcs-sgai.org/ai2010/?section=proceedings
Appears in Collections:Conference Papers and Presentations (Mechanical, Electrical and Manufacturing Engineering)

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