Prasenjit nath (mca, ugc-net) Abstract

Expert systems (ES) were introduced by researchers in the Standford Heuristic Programming Project, including the “father of expert systems” Edwars Feigenbaum, with the DENDRAL and MYCIN systems. Principal contributors to the technology were Bruce Buchanan, Edward Shortlife, Randall Davis, William venMelle, Crali Scott, and others at Stanford.
Research is also very active in France, where researchers focus on the automation of reasoning and logic engines. French Prolog computer language, designed in 1972, marks a real advance over expert systems like DENDRAL or MYCIN. It is a shell, that’s to say a software structure ready to receive any expert system and to run it. It integrates an engine using First-Order logic, with rules and facts. It’s also a declarative language. It’s a tool for mass production of expert systems. It became later, more likely, the best selling AI language in the world. But, Prolog is not so user friendly and uses an one order logic far from human logic.
In the 1980s, expert systems proliferated as they were recognized as a practical tool for solving real-world problems. Universities offered expert system courses and two thirds of the Fortune 1000 companies applied the technology in daily business activities. The expert system internationally became popular with the Fifth Generation Computer Systems Project in Japan and increased research funding in Europe. Growth in the field continued into the 1990s.
Today, they exist in many forms, from medical diagnosis to investment analysis and from counseling to production control. Due to the advances of the last decade, today's expert systems users can choose from dozens of commercial software packages. At present, we accept as routine such expert systems as weather forecasting, online mapping and driving directions, diagnostic systems for automotive repair shops, and so on.
An expert system typically consists of four major components:
1. Knowledge Base: This is the knowledge in the expert system, coded in a form that the system can use. It is developed by some combination of humans (for example, a knowledge engineer) and an automated learning system (for example, one that can learn through the analysis of good examples of an expert’s performance).