Mathematics of Computing -- Numerical Analysis.

Expert Systems for Engineering Design presents the application of expert system methods to a variety of engineering design problems. This book provides the technical details on how the methods are used to solve specific design problems in chemical engineering, civil engineering, and several others. Organized into 12 chapters, this book begins with an overview of the synthesis, the creation, and development of alternative designs. This text then examines the nature of design expertise and the types of computer tools that can enhance the expert's decision-making. Other chapters consider the integration of tools into intelligent, cooperative frameworks. This book discusses as well the use of graphic interfaces with built-in knowledge about the designs being configured. The final chapter deals with the development of software tools for automatic design synthesis and evaluation within the integrated framework of a computer-aided mechanical design system known as CASE, which stands for computer-aided simultaneous engineering. This book is a valuable resource for engineers and architects.

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.

This volume contains the papers, updated in some cases, presented at the first AISMC (Artificial Intelligence and Symbolic Mathematical Computations)conference, held in Karlsruhe, August 3-6, 1992. This was the first conference to be devoted to such a topic after a long period when SMC made no appearance in AI conferences, though it used to be welcome in the early days of AI. Some conferences were held recently on mathematics and AI, but none was directly comparable in scope to this conference. Because of the novelty of the domain, authors were given longer allocations of time than usual in which to present their work. As a result, extended and fruitful discussions followed each paper. The introductory chapter in this book, which was not presented during the conference, reflects in many ways the flavor of these discussions and aims to set out the framework for future activities in this domain of research. In addition to the introduction, the volume contains 20 papers.

This book is about the role of expert systems in marketing, particularly in the consumer goods industry. Section I describes the changing nature of consumer marketing and presents the rationale and need for expert systems. The remainder of the book combines a tutorial on expert systems with a series of expert system prototypes. The tutorial material is presented in three places. First, section II is devoted to introducing expert systems in general. Chapter 3 provides a general introduction to the topic, which is continued in chapter 4 where a small expert system (the Promotion Advisor) is used to illustrate the important features of a backward-chaining, rule-based system. The promotion theme is extended in chapter 5 where a larger system is presented. The material in all three of these chapters was designed as an introduction and tutorial on the most common technology for building applied expert systems: the backward-chaining, rule-based inference engine. Tutorial material is also contained in the body of the chapters that describe the prototypes. This material is usually in the form of sample rules and a description of the process for applying the rules. The third location of the expert system material is in chapters that follow discussions of the prototypes. Chapter 7 is a technical chapter on the coupling of expert systems to traditional systems.