Meta-level knowledge: Overview and applications


"We define the concept of meta-level Knowledge, and illustrate it by briefly reviewing four examples that have been described in detail elsewhere. The examples include applications of the idea to tasks such as transfer of expertise from a domain expert to a program, and the maintenance and use of large Knowledge bases. We explore common themes that arise from these examples, and examine broader implications of the idea, in particular its impact on the design and construction of large programs."IJCAI 5, 920-927

The Art of Artificial Intelligence: Themes and Case Studies of Knowledge Engineering


See also: Stanford Heuristic Programming Project Memo HPP-77-25Proc. IJCAI-77: Fifth International Joint Conference on Artificial Intelligence, pp 1014-1029

Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project


Artificial intelligence, or AI, is largely an experimental science—at least as much progress has been made by building and analyzing programs as by examining theoretical questions. MYCIN is one of several well-known programs that embody some intelligence and provide data on the extent to which intelligent behavior can be programmed. As with other AI programs, its development was slow and not always in a forward direction. But we feel we learned some useful lessons in the course of nearly a decade of work on MYCIN and related programs. In this book we share the results of many experiments performed in that time, and we try to paint a coherent picture of the work. The book is intended to be a critical analysis of several pieces of related research, performed by a large number of scientists. We believe that the whole field of AI will benefit from such attempts to take a detailed retrospective look at experiments, for in this way the scientific foundations of the field will gradually be defined. It is for all these reasons that we have prepared this analysis of the MYCIN experiments.

The complete book in a single file.

EMYCIN: A Knowledge Engineer’s Tool for Constructing Rule-Based Expert Systems


This chapter from the Mycin book is a brief overview of van Melle's Ph.D. dissertation (Stanford, Computer Science), and is a shortened and edited version of a paper appearing in Pergamon-lnfotech state of the art report on machine intelligence, pp. 249-263. Maidenhead, Berkshire, U.K.: Infotech Ltd., 1981. Mycin Book (1984)

Al Magazine 25

AI Magazine

Packet Radio Terminal System Evaluation Tom Ellis and Steve Saunders Work intended to result in a demonstration-level portable terminal to test and evaluate various solutions to the issues raised by extreme portability in the packet-radio environment. The Stanford Heuristic Programming Project: Goals and Activities by the Staff of the Heuristic Programming Project The Heuristic Programming Project (HPP) of the Stanford University Computer Science Department is a laboratory of about fifty people-faculty, staff, and graduate studentswhose main goals are these: model, and thereby to gain a deep understanding of, the nature of scientific reasoning processes in various types of scientific problems, and various areas of science and medicine; part of the methodology, and as a coordinate activity, to construct "Expert Systems"-programs that achieve high levels of performance on tasks that normally require significant human expertise for their solutidn; the HPP therefore has a natural applications orientation. The HPP was started by Professor Edward A. Feigenbaum and Professor Joshua Lederberg (now President, Rockefeller University) as the DENDRAL project in 1965. Professor Bruce Buchanan joined shortly thereafter, and is Co-Principal Investigator of the HPP. For its computing facilities, the HPP uses the Stanfordbased SUMEX-AIM National Resource for Applications of AI to Medicine and Biology (a pair of DEC KI-10s and a DEC 2020); and the SU-SCORE machine (a DEC 2060).