Expert Systems (ES), also called Knowledge-Based Systems (KBS) or simply Knowledge Systems (KS), are computer programs that use expertise to assist people in performing a wide variety of functions, including diagnosis, planning, scheduling and design. ESs are distinguished from conventional computer programs in two essential ways (Barr, Cohen et al. 1989):

  1. Expert systems reason with domain-specific knowledge that is symbolic as well as numerical;
  2. Expert systems use domain-specific methods that are heuristic (i.e., plausible) as well as algorithmic (i.e., certain).

Expert systems have become the most successful commercial applications of Artificial Intelligence (AI) research, first in the United States, and then in Europe and Asia. Thousands of systems are now in routine use world-wide, and span the full spectrum of activities in business, industry and government. Economic gain has been realized along many dimensions: speed-up of professional (and semi-professional) work; internal cost savings on operations; improved quality and consistency of decision making; increased revenue from new products and services; captured organizational know-how; improvements in the way a company does its business; crisis management; and stimulation of innovation.

From a business perspective, the expert systems industry in the U.S. consists of many small companies, or divisions of larger companies, which are selling both expert system development software and support services for assisting users in using that software or developing expert systems. Typical annual revenues for a small ES company or division of a larger company are in the range of $5 to $20 million a year per company. The aggregate total of such sales worldwide is in the range of several hundred million dollars per year.

The technology of expert systems has had a far greater impact than even the expert systems business. Expert system technology has become widespread and deeply embedded. As expert system techniques have matured into a standard information technology, the most important recent trend is the increasing integration of this technology with conventional information processing, such as data processing or management information systems.

Study Objectives

The primary objectives of this JTEC panel were to investigate Japanese expert systems development from both technological and business perspectives and to compare progress and trends with similar developments in the United States. More specifically, there were five dimensions to the study, namely, to investigate:

  1. Business sector applications of expert systems
  2. Infrastructure and tools for expert system development
  3. Advanced knowledge-based systems in industry
  4. Advanced knowledge-based systems research in universities
  5. National projects, including:

The JTEC panel visited 19 sites during its one-week visit in Japan (March 23-27, 1992), and conferred with other Japanese computer scientists and business executives both before and after the official visits. The panel visited four major computer manufacturers, eight companies that are applying expert systems to their operations, three universities, three national projects, and the editors of Nikkei AI, a publication that conducts an annual survey of expert systems applications in Japan.


The panel drew a number of conclusions from these visits, which are discussed in Chapter 8. A comparison of expert systems activities in Japan and the U.S., drawn from those conclusions, is presented in Tables E.1 and E.2.

Although there are many similarities in both the research and applications activities in Japan and the U.S., the panel observed some noteworthy contrasts:

Table E.1
Comparison of Applications of ES in U.S. and Japan

Table E.2
Comparison of KB Research in U.S. and Japan

Published: May 1993; WTEC Hyper-Librarian