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The integration of an intelligent knowledge-based system into engineering software using the blackboard structure

The integration of an intelligent knowledge-based system into engineering software using the blackboard structure

Petridis, M. and Knight, B. (1996) The integration of an intelligent knowledge-based system into engineering software using the blackboard structure. Advances in Engineering Software, 25 (2-3). pp. 141-147. ISSN 0965-9978 (doi:10.1016/0965-9978(95)00101-8)

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Abstract

Over recent years there has been an increase in the use of generic Computational Fluid Dynamics (CFD) software packages spread across various application fields. This has created the need for the integration of expertise into CFD software. Expertise can be integrated into CFD software in the form of an Intelligent Knowledge-Based System (IKBS). The advantages of integrating intelligence into generic engineering software are discussed with a special view to software engineering considerations. The software modelling cycle of a typical engineering problem is identified and the respective expertise and user control needed for each modelling phase is shown. The requirements of an IKBS for CFD software are discussed and compared to current practice. The blackboard software architecture is presented. This is shown to be appropriate for the integration of an IKBS into an engineering software package. This is demonstrated through the presentation of the prototype CFD software package FLOWES.

Item Type: Article
Additional Information: [1] CMS Ref. No: 96/57.
Uncontrolled Keywords: blackboard, Intelligent Knowledge-Based Systems, (IKBS), artificial intelligence, engineering software
Subjects: Q Science > QA Mathematics > QA76 Computer software
T Technology > TA Engineering (General). Civil engineering (General)
Pre-2014 Departments: School of Computing & Mathematical Sciences
School of Computing & Mathematical Sciences > Computer & Computational Science Research Group
School of Computing & Mathematical Sciences > Department of Computer Science
Related URLs:
Last Modified: 14 Oct 2016 09:00
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/389

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