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."

The main concern of this work is whether morphemes play a role in the lexical representation and processing of several types of polymorphemic words and, more particularly, at what precise representational and processing level. The book comprises two theoretical contributions and a number of empirical ones. One theoretical paper discusses several possible motivations for a morphologically organised mental lexicon (like the economy of representation view, and the efficiency of processing view), and lays out the weaknesses that are associated with some of these motivations. The other theoretical paper offers an interactive-activation reinterpretation of the findings that were originally reported within the lexical search framework. The empirical papers together cover a relatively broad array of language types and mainly deal with visual word recognition in normals in the context of lexical morphology (derived and compound words). Evidence is reported on the function of stems and affixes as processing units in prefixed and suffixed derivations. The role of semantic transparency in the lexical representation of compounds is studied, as is the effect of orthographic ambiguity on the parsing of novel compounds. The inflection-derivational distinction is approached in the context of Finnish, a highly agglutinative language with much richer morphology than the languages usually studied in psycholinguistic experiments on polymorphemic words. Two other contributions also approach the study object in the context of relatively uncharted domains: one presents data on Chinese, a language which uses a different script-type (logographic) from the languages that are usually studied (alphabetic script), and another one presents data on language production.

The main concern of this work is whether morphemes play a role in the lexical representation and processing of several types of polymorphemic words and, more particularly, at what precise representational and processing level. The book comprises two theoretical contributions and a number of empirical ones. One theoretical paper discusses several possible motivations for a morphologically organised mental lexicon (like the economy of representation view, and the efficiency of processing view), and lays out the weaknesses that are associated with some of these motivations. The other theoretical paper offers an interactive-activation reinterpretation of the findings that were originally reported within the lexical search framework. The empirical papers together cover a relatively broad array of language types and mainly deal with visual word recognition in normals in the context of lexical morphology (derived and compound words). Evidence is reported on the function of stems and affixes as processing units in prefixed and suffixed derivations. The role of semantic transparency in the lexical representation of compounds is studied, as is the effect of orthographic ambiguity on the parsing of novel compounds. The inflection-derivational distinction is approached in the context of Finnish, a highly agglutinative language with much richer morphology than the languages usually studied in psycholinguistic experiments on polymorphemic words. Two other contributions also approach the study object in the context of relatively uncharted domains: one presents data on Chinese, a language which uses a different script-type (logographic) from the languages that are usually studied (alphabetic script), and another one presents data on language production.

In June of 1983, our expert systems research group at Carnegie Mellon University began to work actively on automating knowledge acquisition for expert systems. In the last five years, we have developed several tools under the pressure and influence of building expert systems for business and industry. These tools include the five described in chapters 2 through 6 - MORE, MOLE, SALT, KNACK and SIZZLE. One experiment, conducted jointly by developers at Digital Equipment Corporation, the Soar research group at Carnegie Mellon, and members of our group, explored automation of knowledge acquisition and code development for XCON (also known as R1), a production-level expert system for configuring DEC computer systems. This work influenced the development of RIME, a programming methodology developed at Digital which is the subject of chapter 7. This book describes the principles that guided our work, looks in detail at the design and operation of each tool or methodology, and reports some lessons learned from the enterprise. of the work, brought out in the introductory chapter, is A common theme that much power can be gained by understanding the roles that domain knowledge plays in problem solving. Each tool can exploit such an understanding because it focuses on a well defined problem-solving method used by the expert systems it builds. Each tool chapter describes the basic problem-solving method assumed by the tool and the leverage provided by committing to the method."

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."