What follows is a sampler of work in knowledge acquisition. It comprises three technical papers and six guest editorials. The technical papers give an in-depth look at some of the important issues and current approaches in knowledge acquisition. The editorials were pro duced by authors who were basically invited to sound off. I've tried to group and order the contributions somewhat coherently. The following annotations emphasize the connections among the separate pieces. Buchanan's editorial starts on the theme of "Can machine learning offer anything to expert systems?" He emphasizes the practical goals of knowledge acquisition and the challenge of aiming for them. Lenat's editorial briefly describes experience in the development of CYC that straddles both fields. He outlines a two-phase development that relies on an engineering approach early on and aims for a crossover to more automated techniques as the size of the knowledge base increases. Bareiss, Porter, and Murray give the first technical paper. It comes from a laboratory of machine learning researchers who have taken an interest in supporting the development of knowledge bases, with an emphasis on how development changes with the growth of the knowledge base. The paper describes two systems. The first, Protos, adjusts the training it expects and the assistance it provides as its knowledge grows. The second, KI, is a system that helps integrate knowledge into an already very large knowledge base.

Inhalt
Introduction: A Sampler in Knowledge Acquisition for the Machine Learning Community.- Can Machine Learning Offer Anything to Expert Systems?.- When Will Machines Learn?.- Supporting Start-to-Finish Development of Knowledge Bases.- The Knowledge Level Reinterpreted: Modeling How Systems Interact.- Automated Knowledge Acquisition for Strategic Knowledge.- The World Would be a Better Place if Non-Programmers Could Program.- Task-Structures, Knowledge Acquisition and Learning.- Automated Support for Building and Extending Expert Models.- Knowledge Acquisition for Knowledge-Based Systems: Notes on the State-of-the Art.