INTRODUCTION TO EXPERT SYSTEMS
1.1What are Expert systems?
1.1.1 A little history
The goal of Artificial intellignce scientists had always been to develop computer programs that could in some sense think, that is solve problems 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 expert systems in their historical context.
In the sixties, AI scientists tried to simulate the complicated process of thinking by finding general methods for solving broad classes of problems; they used these methods in general purpose programs. However despite some interesting progress, this strategy produced no breakthroughs. Developing general purpose programs was too difficult and ultimately fruitless. The more 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 important: the problem solving power of a program comes from the knowledge it possesses, not just the formalisms and inference schemes it employs.
The conceptual breakthrough was made and can be quite simply stated.
To make a program intelligent, provide it with lots of high quality, specific knowledge about some problem area.
The realization led to the development 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
Let's examine the characteristics of an expert system in more detail.
The heart of an expert system is the powerful amount of knowledge that accumulates during system building. The knowledge is explicit and organized to simplify decision making. The importance of this feature of expert system cannot be overemphasized:
The accumulation and codification...