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

Qiang Ji, Michael M. Marefat, Paul J.A. Lever

Research output: Contribution to conferencePaper

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)
Number of pages1
StatePublished - Dec 1 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

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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    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. Paper presented at Proceedings of the 12th National Conference on Artificial Intelligence. Part 1 (of 2), Seattle, WA, USA, .