{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:54:33Z","timestamp":1750308873264,"version":"3.41.0"},"reference-count":5,"publisher":"Association for Computing Machinery (ACM)","issue":"108","license":[{"start":{"date-parts":[[1989,4,1]],"date-time":"1989-04-01T00:00:00Z","timestamp":607392000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGART Bull."],"published-print":{"date-parts":[[1989,4]]},"abstract":"<jats:p>\n            Instead of acquiring diagnostic rules about a manufacturing process directly from domain experts, one can acquire a deeper, model-based representation, and compile the diagnostic rules directly from it. A manufacturing process representation (MPR) is a\n            <jats:italic>deep<\/jats:italic>\n            (Harmon, 1985), functional representation (Davis, 1984; Chandrasekaran, 1985) which embodies a model of the given process. This representation includes knowledge about the ordering of process steps, as well as what they are designed to accomplish and what would make them fail or what errors they may trap. As indicated below, an MPR can be acquired either by a knowledge engineer (KE), who would interview an expert and make use of any available process design documents, or by an Intelligent Interrogator (II) program.\n          <\/jats:p>","DOI":"10.1145\/63266.63304","type":"journal-article","created":{"date-parts":[[2007,1,17]],"date-time":"2007-01-17T18:32:02Z","timestamp":1169058722000},"page":"172-173","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Using simulation to compile diagnostic rules from a manufacturing process representation"],"prefix":"10.1145","author":[{"given":"L. A.","family":"Becker","sequence":"first","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, MA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R.","family":"Barlett","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, MA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"F.","family":"Soroushian","sequence":"additional","affiliation":[{"name":"Worchester Polytechnic Institute, Worcester, MA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[1989,4]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"83","volume-title":"Distributed diagnosis of execution errors in a manufacturing system,\" in Knowledge Based Systems for Engineering: Classification, Education, and Control. D. Sriram and R. A. Adey (Eds)","author":"Becker L. A.","year":"1987","unstructured":"Becker , L. A. , and A. Kinigadner ( 1987 ). \" Distributed diagnosis of execution errors in a manufacturing system,\" in Knowledge Based Systems for Engineering: Classification, Education, and Control. D. Sriram and R. A. Adey (Eds) . Boston, MA : Computational Mechanics Publications , pp. 83 - 93 . Becker, L. A., and A. Kinigadner (1987). \"Distributed diagnosis of execution errors in a manufacturing system,\" in Knowledge Based Systems for Engineering: Classification, Education, and Control. D. Sriram and R. A. Adey (Eds). Boston, MA: Computational Mechanics Publications, pp. 83-93."},{"key":"e_1_2_1_2_1","first-page":"285","volume-title":"J. Gero (Ed.)","author":"Becker L. A.","year":"1988","unstructured":"Becker , L. A. , R. Bartlett , and M. Roy ( 1988 ). \" Compiling diagnostic rules from a manufacturing process representation,\" in Artificial Intelligence in Engineering: Diagnosis and Learning , in J. Gero (Ed.) , Southhampton : Computational Mechanics Publications , pp. 285 - 305 . Becker, L. A., R. Bartlett, and M. Roy (1988). \"Compiling diagnostic rules from a manufacturing process representation,\" in Artificial Intelligence in Engineering: Diagnosis and Learning, in J. Gero (Ed.), Southhampton: Computational Mechanics Publications, pp. 285-305."},{"doi-asserted-by":"publisher","key":"e_1_2_1_3_1","DOI":"10.1145\/1056557.1056561"},{"doi-asserted-by":"publisher","key":"e_1_2_1_4_1","DOI":"10.1016\/0004-3702(84)90042-0"},{"key":"e_1_2_1_5_1","volume-title":"Expert Systems: Artificial Intelligence in Business","author":"Harmon P.","year":"1985","unstructured":"Harmon , P. , and D. King ( 1985 ). Expert Systems: Artificial Intelligence in Business , New York, NY : John Wiley . Harmon, P., and D. King (1985). Expert Systems: Artificial Intelligence in Business, New York, NY: John Wiley."}],"container-title":["ACM SIGART Bulletin"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/dl.acm.org\/doi\/10.1145\/63266.63304","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/dl.acm.org\/doi\/pdf\/10.1145\/63266.63304","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T21:15:16Z","timestamp":1750281316000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/dl.acm.org\/doi\/10.1145\/63266.63304"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1989,4]]},"references-count":5,"journal-issue":{"issue":"108","published-print":{"date-parts":[[1989,4]]}},"alternative-id":["10.1145\/63266.63304"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/63266.63304","relation":{},"ISSN":["0163-5719"],"issn-type":[{"type":"print","value":"0163-5719"}],"subject":[],"published":{"date-parts":[[1989,4]]},"assertion":[{"value":"1989-04-01","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}