Knowledge Acquisition for Expert Systems: A Practical Handbook
Springer US, Jul 31, 1987 - Psychology - 208 pages
Building an expert system involves eliciting, analyzing, and interpreting the knowledge that a human expert uses when solving problems. Expe rience has shown that this process of "knowledge acquisition" is both difficult and time consuming and is often a major bottleneck in the production of expert systems. Unfortunately, an adequate theoretical basis for knowledge acquisition has not yet been established. This re quires a classification of knowledge domains and problem-solving tasks and an improved understanding of the relationship between knowledge structures in human and machine. In the meantime, expert system builders need access to information about the techniques currently being employed and their effectiveness in different applications. The aim of this book, therefore, is to draw on the experience of AI scientists, cognitive psychologists, and knowledge engineers in discussing particular acquisition techniques and providing practical advice on their application. Each chapter provides a detailed description of a particular technique or methodology applied within a selected task domain. The relative strengths and weaknesses of the tech nique are summarized at the end of each chapter with some suggested guidelines for its use. We hope that this book will not only serve as a practical handbook for expert system builders, but also be of interest to AI and cognitive scientists who are seeking to develop a theory of knowledge acquisition for expert systems.
What people are saying - Write a review
We haven't found any reviews in the usual places.
Other editions - View all
algorithm allows analysis application approach Artificial Intelligence asked attributes behavior blood building causal chapter complete concepts considered constraints construct decision described detail developed diagnosis discussed domain effective elements elicitation example expert system expertise explanation express facts Figure flow fluid formal functions Gaines give given grid human identify implementation important induction inference interpretation interview involved KADS knowledge acquisition knowledge base knowledge engineer less mechanism methodology methods nature norm(std objects original particular performance planning possible practice present problem procedure produced protocol qualitative quantities questions reasoning reference relations relationships relevant represent representation Research rules Science selected Shaw shows similar simulation solving spaces specific stage strategy structure suggests Table task teachback techniques tion transcript tree