Relay-Version: version B 2.10 5/3/83; site utzoo.UUCP Path: utzoo!watmath!clyde!rutgers!mit-eddie!genrad!decvax!UCBVAX.BERKELEY.EDU!ashutosh%euler.Berkeley.EDU From: ashutosh%euler.Berkeley.EDU@UCBVAX.BERKELEY.EDU.UUCP Newsgroups: mod.ai Subject: Seminar - Expert Systems in Manufacturing (UCB) Message-ID: <8702030029.AA22387@euler.Berkeley.EDU> Date: Mon, 2-Feb-87 19:29:11 EST Article-I.D.: euler.8702030029.AA22387 Posted: Mon Feb 2 19:29:11 1987 Date-Received: Sat, 7-Feb-87 10:45:04 EST Sender: daemon@ucbvax.BERKELEY.EDU Organization: The ARPA Internet Lines: 30 Approved: ailist@sri-stripe.arpa CS 298 Seminar Expert Systems for Diagnostic and Control in Manufacturing Prof. Alice M. Agogino Dept. of Mechanical Engineering, UC Berkeley 608-7 Evans, Tuesday Feb.3, 5 - 6 pm. Abtract : An architecture for the hierarchical integration of sensors and diagnostic reasoning in expert systems for automated manufacturing and process control is described. The system architecture uses influence diagrams to provide a symbolic representation of the knowledge obtained from experts with varying degrees of technical proficiency and from diverse domains of expertise. The symbolic representation also maps to a functional level of knowledge which can be used by the knowledge acquistion system to obtain a more detailed numerical level of information from experts , maintenance records, statistical data bases or sensor signals. The diagnostic implementation uses probailistic inference to answer questions concerning possible failures in an automated manufacturing or process system based on observable sensor readings. A search through the influence diagram network provides the topological solution or calculation sequence to answer any such diagnostic query. Once the topological and numerical solution to the influence diagram has been determined, qualitative and quantitative advice can be relayed to the controller , operator or diagnostician. A description of an implementation of such an architecture will be provided.