Relay-Version: version B 2.10 5/3/83; site utzoo.UUCP Posting-Version: version B 2.10.2 9/18/84 +MULTI+2.11; site brueer.UUCP Path: utzoo!linus!philabs!cmcl2!seismo!mcvax!ukc!reading!brueer!holte From: holte@brueer.UUCP (Robert Holte) Newsgroups: net.ai Subject: Re: inductive learning in expert systems Message-ID: <200@brueer.brueer.UUCP> Date: Mon, 12-Aug-85 02:59:49 EDT Article-I.D.: brueer.200 Posted: Mon Aug 12 02:59:49 1985 Date-Received: Thu, 15-Aug-85 08:32:59 EDT References: <770@utcs.UUCP> Organization: Dept of EE & E, Brunel University, Uxbridge, U.K. Lines: 45 Xpath: reading gateway.cs The only system I know of which generates rules for a knowledge base on strictly probabilistic grounds is the RX system (R.L. Blum,IJCAI,1983). The use of AI machine learning techniques to generate a knowledge base from examples is strongly advocated by Donald Michie, who has made commercially available knowledge engineering tools (e.g. EXPERT EASE, and RULEMAKER) for this purpose. There has not been much published on the use of these tools, and I would very much like to hear from anyone who has experience using them. Michalski reported a striking case in which the knowledge base induced from examples significantly outperformed those which had been engineered in close collaboration with a human expert (International Journal of Man-Machine Studies, vol.12, pp.63-87, 1980, and elsewhere), but he does not appear to have carried out any similar studies since. A rule-learning module was included in his ADVISE system for knowledge engineering, but I have not heard anything about ADVISE since it was announced at IJCAI in 1983. The successful domain was soybean disease diagnosis, and I believe a recently begun ESPRIT project is attempting to extend this work to a wide class of European plants and plant diseases. I attended the International Machine Learning Workshop in June. The topic of generating knowledge-bases from examples was discussed only once, by Bruce Buchanan in his overview of learning research at Stanford. Of the sixty-plus research summaries included in the workshop proceedings, only a handful were concerned with this topic, namely those coming under the general heading of Learning Apprentices. Learning Apprentices learn new control (meta ?) knowledge or correct existing knowledge by observing a human expert solving problems with the aid of a reasoning assistant (e.g. an ordinary knowledge-based system): they are not intended to create new knowledge bases. Most of the existing LAs will be described at IJCAI in Los Angeles (1985). On the other hand, I have seen a proposal (not yet approved) for a joint European project which specifically includes the general topic of automatic knowledge acquisition for expert systems, and I have heard a rumor that there will be a (relatively small) commercially-backed project on this topic in Britain starting this year. -- Rob Holte holte%brueer@ucl-cs {...ENGLAND}!ukc!reading!brueer!holte Tower C Brunel University Uxbridge England UB8 3PH