Please use this identifier to cite or link to this item:
doi:10.22028/D291-25225
Title: | A consequence-finding approach for feature recognition in CAPP |
Author(s): | Hinkelmann, Knut |
Language: | English |
Year of Publication: | 1994 |
OPUS Source: | Kaiserslautern ; Saarbrücken : DFKI, 1994 |
SWD key words: | Künstliche Intelligenz |
DDC notations: | 004 Computer science, internet |
Publikation type: | Report |
Abstract: | We present a rewriting approach for a consequence-finding inference of logic programs. Consequence finding restricts the derivations of a logic program to exactly those facts that depend on an explicitly given set of initial facts. The rewriting approach extends the Generalized Magic Sets rewriting, well-known from deductive databases, by an up propagation in addition to the usual down propagation. The initial motivation for this inference was to realize the abstraction phase of a knowledge-based CAPP system for lathe turning. The input to the CAPP system is a detailed description of a workpiece. During the abstraction phase characteristic parts, called features, are recognized for which predefined skeletal plans exist. Consequence finding is a method to restrict the computation such that exactly the features of the actual workpiece are derived. The same inference can also be used for checking integrity constraints: given an update of a deductive database or a logic program, consequence finding applies only those rules that are effected by the update operation. |
Link to this record: | urn:nbn:de:bsz:291-scidok-50425 hdl:20.500.11880/25281 http://dx.doi.org/10.22028/D291-25225 |
Series name: | Research report / Deutsches Forschungszentrum für Künstliche Intelligenz [ISSN 0946-008x] |
Series volume: | 94-11 |
Date of registration: | 7-Feb-2013 |
Faculty: | SE - Sonstige Einrichtungen |
Department: | SE - DFKI Deutsches Forschungszentrum für Künstliche Intelligenz |
Collections: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
Files for this record:
File | Description | Size | Format | |
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RR_94_11_.pdf | 3,53 MB | Adobe PDF | View/Open |
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