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 language | English (US) |
|---|---|
| Title of host publication | Proceedings of the National Conference on Artificial Intelligence |
| Publisher | AAAI |
| Pages | 1462 |
| Number of pages | 1 |
| Volume | 2 |
| Publication status | Published - 1994 |
| Event | Proceedings of the 12th National Conference on Artificial Intelligence. Part 1 (of 2) - Seattle, WA, USA Duration: Jul 31 1994 → Aug 4 1994 |
Other
| Other | Proceedings of the 12th National Conference on Artificial Intelligence. Part 1 (of 2) |
|---|---|
| City | Seattle, WA, USA |
| Period | 7/31/94 → 8/4/94 |
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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.