Overview: Knowledge-based expert systems, or simply expert systems, use human knowledge to solve problems that normally would require human intelligence. These expert systems represent the expertise knowledge as data or rules within the computer. These rules and data can be called upon when needed to solve problems. Books and manuals have a tremendous amount of knowledge but a human has to read and interpret the knowledge for it to be used. Conventional computer programs perform tasks using conventional decision-making logic -- containing little knowledge other than the basic algorithm for solving that specific problem and the necessary boundary conditions.

This chapter from The Age of Intelligent Machines (published in 1990) addresses the history and development of AI, and where it was headed, circa 1990. Edward Feigenbaum is a Professor of Computer Science and Co-Scientific Director of the Knowledge Systems Laboratory at Stanford University. Dr. Feigenbaum served as Chief Scientist of the United States Air Force from 1994 to 1997. It has been said that when people make forecasts, they overestimate what can be done in the short run and underestimate what can be achieved in the long run. I have worked in the science and technology of artificial intelligence for twenty years and confess to being chronically optimistic about its progress. The gains have been substantial, even impressive. But we have hardly begun, and we must not lose sight of the point to which we are heading, however distant it may seem.

Jeff Pepper is product marketing manager of the diagnostics division of the Carnegie Group. His current research interests include all aspects of knowledge-base design, construction, validation, and maintenance. He has published several articles on building expert systems and integrating them into service organizations. He has also taught computer programming at Carnegie Mellon University. Big changes are coming to the world of automobile repair. In the Ford dealership of the not too distant future, mechanics will be tracking down and fixing problems in your car with the help of a powerful AI diagnostic tool called SBDS, under development by a joint project team at Ford Motor Company, the Carnegie Group, and Hewlett Packard. SBDS, the Service Bay Diagnostic System, will revolutionize the way cars are fixed.

LQMS is a knowledge-based system that identifies and explains anomalies in data acquired from multiple sensors. The knowledge base was built by a sequence of domain experts. Its prototype performed with a high level of accuracy and that performance has been incrementally and significantly improved during development and field testing. Several points are developed in this paper. This enticed them to become more directly involved, and, resulted in a better system.

A legal knowledge based system called JUSTICE is presented which can identify heterogeneous representations of concepts across all major Australian jurisdictions, and some concepts within US and UK cases. The knowledge representation scheme used for legal and common sense concepts is inspired by human processes for the identification of concepts and the expected order and location of concepts. These are supported by flexible search functions and various string utilities. JUSTICE is a client-based legal software agent which works with both plaintext and HTML representations of legal cases over file systems, and the World Wide Web. In creating JUSTICE an ontology for legal cases was developed, and this is implicit within JUSTICE.

The effective use of argument is, of course, central to the practice of Law, and it is important that students of Law learn this skill. We describe here the architecture of a computer-based system to enable students to practice argumentation in a regulated environment. The system makes use of the concept of a dialogue game as a means of providing the necessary rule-governed structure for the conduct of an argument between two students, or a student and a teacher. The architecture described is generic in that it can be instantiated with different forms of dialogue game. This instantiation is achieved by the use of performatives to specify the rules of the game and the semantics of operations within the Dialogue Abstract Machine that is used to implement it.

An ES tool provides one or more knowledge representation schemes for expressing knowledge about the application domain. Some tools use both frames (objects) and IF-THEN rules. In PROLOG the knowledge is represented as logical statements. Reasoning engine: Inference mechanisms for manipulating the symbolic information and knowledge in the knowledge base to form a line of reasoning in solving a problem. The inference mechanism can range from simple modus ponens backward chaining of IF-THEN rules to case-based reasoning.

AI programs that achieve expert-level competence in solving problems in task areas by bringing to bear a body of knowledge about specific tasks are called knowledge-based or expert systems. Often, the term expert systems is reserved for programs whose knowledge base contains the knowledge used by human experts, in contrast to knowledge gathered from textbooks or non-experts. More often than not, the two terms, expert systems (ES) and knowledge-based systems (KBS), are used synonymously. Taken together, they represent the most widespread type of AI application. The area of human intellectual endeavor to be captured in an expert system is called the task domain.

A computer application that performs a task that would otherwise be performed by a human expert. For example, there are expert systems that can diagnose human illnesses, make financial forecasts, and schedule routes for delivery vehicles. Some expert systems are designed to take the place of human experts, while others are designed to aid them. Expert systems are part of a general category of computer applications known as artificial intelligence . To design an expert system, one needs a knowledge engineer, an individual who studies how human experts make decisions and translates the rules into terms that a computer can understand.