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A self-learning case and rule-based reasoning algorithm for intelligent technology evaluation and selection [Abstract]

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conference contribution
posted on 2017-06-09, 12:12 authored by Liam Evans, Niels LohseNiels Lohse, Phil Webb
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.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

AI-2010 Thirtieth SGAI International Conference on Artificial Intelligence

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.

Version

  • AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2010

Notes

This is an abstract of a conference paper.

Language

  • en

Location

Cambridge, UK