First published in 1992, this volume identifies the problems facing the designer of multi-environmental knowledge-based systems, and explains the principles that must be followed in order to obtain successful results. Systems called upon to function in a variety of widely differing cultural and natural environments can only do so satisfactorily if from the very beginning they have been designed with this versatility in mind. For the first time, the know-how for this often formidable design task has been gathered together and presented here. This study was written to an overall plan, with chapters commissioned from a group of research of quite diversified back-grounds who had deeply explored their subjects. Each topic was thus covered in close connection with the others, so as to form a coherent whole. While primarily aimed at workers in Artificial Intelligence and Expert Systems, as well as designers of other kinds of sophisticated software, the contents of the book are of wider validity, just as the multi-environmental demands are of wider incidence. Manufactures, exporters and importers of computing technology with a large knowledge component will also find their concerns addressed.

This collection of previously published papers brings together state-of-the-art developments in expert system testing. The volume is separated into five chapters on expert system validation, knowledge base verification, development and evaluation, case studies and tools, and general topics. The pape

Fuzzy Logic for the Management of Uncertainty covers many important topics, including:" "Developments in mathematics that have paved the road for fuzzy logic;" "Deep, and of a broad perspective, exposition of virtually all approaches used in contemporary science for the representation and handling of imperfect (uncertain, imprecise, vague, ambiguous, etc.) information;" "Coverage of practically all relevant and promising directions and approaches in fuzzy logic research including LT--fuzzy logic, model theoretic approaches, intuitionistic fuzzy logic, nonmonotonic fuzzy logic, modifier fuzzy logic;" "VLSI fuzzy logic-based chips that have triggered the implementation of fuzzy logic in so many fields of science and technology;" "A broad coverage of fuzzy logic in approximate reasoning, including basic issues related to the role of fuzzy logic for approximate reasoning, analyses of various definitions of fuzzy implication that is a crucial element in fuzzy logic-based reasoning schemes,

Knowledge-based systems are increasingly found in a wide variety of settings and this handbook has been written to meet a specific need in their widening use. While there have been many successful applications of knowledge-based systems, some applications have failed because they never received the corrective feedback that evaluation provides for keeping development focused on the users' needs in their actual working environment. This handbook provides a conceptual framework and compendium of methods for performing evaluations of knowledge-based systems during their development. Its focus is on the users' and subject matter experts' evaluation of the usefulness of the system, and not on the developers' testing of the adequacy of the programming code. The handbook permits evaluators to systematically answer the following kinds of questions: Does the knowledge-based system meet the users' task requirements? Is the system easy to use? Is the knowledge base logically consistent? Does it meet the required level of expertise? Does the system improve performance? The authors have produced a handbook that will serve two audiences: a tool that can be used to create knowledge-based systems (practitioners, developers, and evaluators) and a framework that will stimulate more research in the area (academic researchers and students). To accomplish this, the handbook is built around a conceptual framework that integrates the different types of evaluations into the system of development process. The kinds of questions that can be answered, and the methods available for answering them, will change throughout the system development life cycle. And throughout this process, one needs to know what can be done, and what can't. It is this dichotomy that addresses needs in both the practitioner and academic research audiences.