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A multi-agent based knowledge search framework to support the product development process

A multi-agent based knowledge search framework to support the product development process

Jian, Guo, Gao, James and Wang, Yinglin (2010) A multi-agent based knowledge search framework to support the product development process. International Journal of Computer Integrated Manufacturing, 23 (3). pp. 237-247. ISSN 0951-192X (doi:10.1080/09511920903529222)

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Abstract

The amount of information available via networks and databases highlights the limited assistance of existing search and retrieval engines in locating relevant information. Owing to the rising demand for information retrieval, manufacturers employ various search techniques to provide a more satisfied information retrieval performance in its domain.

As a knowledge-intensive activity, a product development team seeks a more effective knowledge search to achieve competitive advantage. This paper proposes a knowledge search methodology using autonomous, intelligent agents to transform passive search and retrieval engines into active, personal assistants for the product development process.

The combination of effective information retrieval techniques and autonomous, intelligent agents will improve the performance of short-term information retrieval in existing search or retrieval engines. Results of the research project will be presented and discussed. This
includes a multi-agent approach for knowledge search, the selection of agent construction tools, and the current implementation of the proposed methodology.

Item Type: Article
Uncontrolled Keywords: knowledge search methodology, agent construction tools, information retrieval, product development process,
Subjects: Q Science > Q Science (General)
T Technology > TS Manufactures
Pre-2014 Departments: School of Engineering
School of Engineering > Centre for Innovative Product Development
School of Engineering > Department of Engineering Systems
Related URLs:
Last Modified: 14 Oct 2016 09:09
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/3496

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