Dempster-Shafer and Bayesian networks for CAD-based feature extraction: A comparative investigation and analysis

Qiang Ji, Michael Mahmoud Marefat, Paul J A Lever

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The paper evaluates the performance the Dempster-Shafer theory (DS) and the Bayesian Belief Network (BBN) with regard to their ability to extract manufacturing features from the solid model description of objects.

Original languageEnglish (US)
Title of host publicationProceedings of the National Conference on Artificial Intelligence
PublisherAAAI
Pages1462
Number of pages1
Volume2
Publication statusPublished - 1994
EventProceedings of the 12th National Conference on Artificial Intelligence. Part 1 (of 2) - Seattle, WA, USA
Duration: Jul 31 1994Aug 4 1994

Other

OtherProceedings of the 12th National Conference on Artificial Intelligence. Part 1 (of 2)
CitySeattle, WA, USA
Period7/31/948/4/94

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ASJC Scopus subject areas

  • Software

Cite this

Ji, Q., Marefat, M. M., & Lever, P. J. A. (1994). Dempster-Shafer and Bayesian networks for CAD-based feature extraction: A comparative investigation and analysis. In Proceedings of the National Conference on Artificial Intelligence (Vol. 2, pp. 1462). AAAI.