Relay-Version: version B 2.10 5/3/83; site utzoo.UUCP Path: utzoo!linus!philabs!seismo!hao!hplabs!sri-unix!DIETTERICH@SUMEX-AIM.ARPA From: DIETTERICH@SUMEX-AIM.ARPA Newsgroups: net.ai Subject: Re: response to response to challenge Message-ID: <14112@sri-arpa.UUCP> Date: Fri, 25-Nov-83 20:40:44 EST Article-I.D.: sri-arpa.14112 Posted: Fri Nov 25 20:40:44 1983 Date-Received: Thu, 1-Dec-83 21:27:06 EST Lines: 71 From: Tom Dietterich Mike, While I would certainly welcome the kinds of controlled studies that you sketched in your msg, I think my claim is correct and can be supported. Virtually every expert system that has been built has been targeted at tasks that were previously untouched by computing technology. I claim that the reason for this is that the proper programming methodology was needed before these tasks could be addressed. I think the key parts of that methodology are (a) a modular, explicit representation of knowledge, (b) careful separation of this knowledge from the inference engine, and (c) an expert-centered approach in which extensive interviews with experts replace attempts by computer people to impose a normative, mathematical theory on the domain. Since there are virtually no cases where expert systems and "traditional" systems have been built to perform the same task, it is difficult to support this claim. If we look at the history of computers in medicine, however, I think it supports my claim. Before expert systems techniques were available, many people had attempted to build computational tools for physicians. But these tools suffered from the fact that they were often burdened with normative theories and often ignored the clinical aspects of disease diagnosis. I blame these deficiencies on the lack of an "expert-centered" approach. These programs were also difficult to maintain and could not produce explanations because they did not separate domain knowledge from the inference engine. I did not claim anywhere in my msg that expert systems techniques are "The Solution to All Our Problems". Certainly there are problems for which knowledge programming techniques are superior. But there are many more for which they are too expensive, too slow, or simply inappropriate. It would be absurd to write an operating system in EMYCIN, for example! The programming advances that would allow operating systems to be written and debugged easily are still undiscovered. You credit fancy LISP environments for making expert systems easy to write, debug, and maintain. I would certainly agree: The development of good systems for symbolic computing was an essential prerequisite. However, the level of program description and interpretation in EMYCIN is much higher than that provided by the Interlisp system. And the "expert-centered" approach was not developed until Ted Shortliffe's dissertation. You make a very good point in your last paragraph: Many military and industry managers who are supporting AI work are going to be very disillusioned in a few years when AI doesn't deliver what has been promised. Unsupported claims about the efficacy of AI aren't going to help. It could hurt our credibility, and thereby our funding and ability to continue the basic research. AI (at least in Japan) has "promised" speech understanding, language translation, etc. all under the rubric of "knowledge-based systems". Existing expert-systems techniques cannot solve these problems. We need much more research to determine what things CAN be accomplished with existing technology. And we need much more research to continue the development of the technology. (I think these are much more important research topics than comparative studies of expert-systems technology vs. other programming techniques.) But there is no point in minimizing our successes. My original message was in response to an accusation that AI had no merit. I chose what I thought was AI's most solid contribution: an improved programming methodology for a certain class of problems. --Tom