Expert Systems Analysis.

CHAPTER 1

INTRODUCTION TO EXPERT SYSTEMS

1.1What atomic number 18 Expert systems?

1.1.1 A little history

The end of Artificial intellignce scientists had always been to develop computer programs that could in whatever sense think, that is solve enigmas in a way that would be considered intelligent if done by a human. Expert systems are the fruit of 20-years quest to define appropriate nature of such programs. Figure 1.1.1 puts undecomposed systems in their historical context.

In the sixties, AI scientists time-tested to simulate the complicated process of thinking by finding usual methods for solving broad classes of problems; they used these methods in general purpose programs. However despite some interesting progress, this system produced no breakthroughs. Developing general purpose programs was too tricky and ultimately fruitless. The much classes of problems a single program could handle. The more poorly it seemed to do on any individual problem.

It was not until the late 1970s that AI scientists began to realize something quite classical: the problem solving power of a program comes from the fellowship it possesses, not just the formalisms and inference schemes it employs. The conceptual breakthrough was do and can be quite simply stated.

To make a program intelligent, provide it with lots of high quality, specific companionship about some problem area.

The realization led to the nurture of special-purpose computer programs that were expert in some narrow problem area. These were called expert systems, and a new field began.

1.1.2 Features of an expert system

Lets screen the characteristics of an expert system in more detail.

The heart of an expert system is the powerful amount of acquaintance that accumulates during system building. The knowledge is explicit and organized to simplify decision making. The importance of this accept of expert system cannot be overemphasized:

The accumulation and codification...

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