Expert Systems.

EXPERT SYSTEMS

OBJECTIVES

On completion of this lesson , you should be able to

- Explain the meaning of expert system.

- Explain the capabilities of expert system.

- Explain the role of knowledge acquisition.

- Explain the importance of knowledge representation.

- Explain the approaches to knowledge representation.

- Explain the issues in knowledge representation.

- Explain what is frame problem ?

- Explain the importance of predicate logic.

- Explain the use of rules in representing knowledge.

- Explain forward versus backward reasoning.

- Explain matching

- Explain the statistical reasoning, fuzzy logic, semantic nets, frames, concept

dependency and scripts.

- Explain case-based reasoning

-Give a short note on a DENDRAL and MYCIN.

Expert systems solve problems that are normally solved by human “experts”. To solve expert level problems.

 

(A) Expert systems need access to a substantial domain knowledge base, which

must be built as efficiently as possible.

(B) Expert systems also need to exploit one or more reasoning mechanisms to

apply their knowledge to the given problems.

(C)Expert systems need a mechanism for explaining that they have done to the

users who rely on them.

(D) Expert systems represent applied AI in a very broad sense.

The problem that expert systems deal with are highly diverse. There are some general issues that arise across varying domains. Also, there are powerful techniques that can be defined for specific classes of problems. Some key problem characteristics play an important role in guiding the design of the problem solving systems. For example, tools that are developed to support one classification or diagnosis task are often useful for another, while different tools are useful for solving various kinds of design tasks.

Expert Systems are complex AI programs. Almost all the techniques of AI (that includes heuristic techniques) are used in expert systems. The most widely used way of representing in expert systems. The most widely used way of representing domain knowledge in expert systems is a set of production rules which are often coupled with a frame system that defines the objects that occur in the rules. MYCIB is one such system.

In an expert system is to be an effective tool, people must be able to interact with it easily . to facilitate this interaction, the expert system must have the following two capabilities in addition to the ability to perform its underlying task.

Explain its Reasoning:-

In many of  the domains in which expert systems operate, people will not accept results unless they have been convinced of the accuracy of the reasoning process that produced those results. This is particularly true, for example, in medicine, where a doctor must accept ultimate responsibility for a diagnosis, even if that diagnosis was arrived at with considerable help from a program. Thus it is important that the reasoning process used in such programs proceed in understandable steps and that enough meta-knowledge (knowledge about reasoning process) be available so that the explanations of those steps can be generated.

(B) Acquire new knowledge and modifications of old knowledge:-

Since expert system derive their power from the richness of the knowledge bases they exploit, it is extremely important that those knowledge bases be as complete and as accurate as possible. But often there exists no standard codification of that knowledge; rather it exists only inside the heads of human experts. One way with human expert. Another way is to have the program learn expert behavior from new data.