Fundamentals of expert systems

This chapter introduces the basic concepts of expert systems. The hierarchical process of developing expert systems is presented, as well as the essential characteristics of expert systems are presented. More specific details of the concepts introduced in this chapter are covered in subsequent chapters. This book is organized in the structure of a strategic process for developing successful expert systems. Figure 2.1 presents the hierarchy of topics as they are presented here and in the subsequent chapters. The strategic process is recommended for anyone venturing into the technology of expert systems from the standpoint of training, research, or applications. This chapter covers the basic concepts of expert systems technology. A basic understanding of these concepts is essential to getting the most out of expert systems. More specific details of the concepts presented in this chapter are discussed in appropriate sections of the subsequent chapters. Chapter 3 covers problem analysis. To be effective, the right problems must be selected for expert systems implementation. The principle of ''garbage in, garbage out'' is also applicable here. Wrong problems lead to incorrect implementation of expert systems. Chapter 4 covers knowledge engineering. Knowledge acquisition is a critical aspect of the expert systems effort. If the knowledge collected is garbage, the best that can be expected from a system is garbage. Chapter 5 presents probabilistic and fuzzy reasoning. Chapter 6 presents fuzzy systems techniques for handling uncertainty in expert systems. Chapter 7 presents neural networks. Chapter 8 covers neural-fuzzy networks. Chapter 9 presents the technique of evolutionary computing. Chapter 10 presents an application to manufacturing. Chapter 11 presents an application to forecasting.

[1]  J. McCarthy.  Some Expert Systems Need Common Sense , 1984, Annals of the New York Academy of Sciences.

[2]  John McCarthy,et al.  Programs with common sense , 1960 .

[3]  Edward H. Shortliffe,et al.  The treatment advice of a computer-based cancer chemotherapy protocol advisor. , 1985, Annals of internal medicine.

[4]  Carl Hewitt,et al.  Viewing Control Structures as Patterns of Passing Messages , 1977, Artif. Intell..

[5]  Hubert L. Dreyfus,et al.  Why Expert Systems Do Not Exhibit Expertise , 1986, IEEE Expert.

[6]  Stephen F. Smith,et al.  ISIS—a knowledge‐based system for factory scheduling , 1984 .

[7]  Joel Moses,et al.  Symbolic integration: the stormy decade , 1966, CACM.

[8]  Randall Davis,et al.  Expert Systems: Where Are We? And Where Do We Go from Here? , 1982, AI Mag..

[9]  Frederick Hayes-Roth,et al.  Building expert systems , 1983, Advanced book program.

[10]  William J. Clancey,et al.  Heuristic Classification , 1986, Artif. Intell..

[11]  G A Gorry,et al.  Modelling the diagnostic process. , 2011, Journal of medical education.

[12]  Reid G. Smith.  On the Development of Commercial Expert Systems , 1984, AI Mag..

[13]  Jaime G. Carbonell,et al.  Machine learning: a guide to current research , 1986 .

[14]  William B. Gevarter.  The Nature and Evaluation of Commercial Expert System Building Tools , 1987, Computer.

[15]  Clive L. Dym,et al.  PRIDE: An Expert System for the Design of Paper Handling Systems , 1986, Computer.

[16]  Terry Winograd,et al.  Understanding natural language , 1974 .

[17]  Jerald L. Feinstein Frederick Siems,et al.  EDAAS: an expert system at the US Environmental Protection Agency for avoiding disclosure of confidential business information , 1985 .

[18]  Roger C. Schank,et al.  The Current State of AI: One Man's Opinion , 1983, AI Mag..