Expert Systems

EXPERT SYSTEMS

One of the largest area of applications of artificial intelligence is in expert systems, or knowledge based systems as they are often known. This type of system seeks to exploit the specialised skills or information held by of a group of people on specific areas. It can be thought of as a computerised consulting service. It can also be called an information guidance system. Such systems are used for prospecting medical diagnosis or as educational aids. They are also used in engineering and manufacture in the control of robots where they inter-relate with vision systems. The initial attempts to apply artificial intelligence to generalised problems made limited progress as we have seen but it was soon realised that more significant progress could be made if the field of interest was restricted.

STRUCTURE

The internal structure of an expert system can be considered to consist of three parts:

the knowledge base ; the database; the rule interpreter.

This is analagous to the production system where we have

the set of productions; the set of facts held as working memory and a rule interpreter.

The knowledge base holds the set of rules of inference that are used in reasoning. Most of these systems use IF-THEN rules to represent knowledge. Typically systems can have from a few hundred to a few thousand rules.

The database gives the context of the problem domain and is generally considered to be a set of useful facts. These are the facts that satisfy the condition part of the condition action rules as the IF THEN rules can be thought of.

The rule interpreter is often known as an inference engine and controls the knowledge base using the set of facts to produce even more facts. Communication with the system is ideally provided by a natural language interface. This enables a user to interact independently of the expert with the intelligent system.

OPERATION OF THE SYSTEM

Again there are three modes to this:

the knowledge acquisition mode;

the consultation mode;

and the explanation mode.

We shall consider each in turn.

KNOWLEDGE ACQUISITION

The system must liaise with people in order to gain knowledge and the people must be specialized in the appropriate area of activity. For example medical doctors, geologists or chemists. The knowledge engineer acts as an intermediary between the specialist and the expert system.

Typical of the information that must be gleaned is vocabulary or jargon, general concepts and facts, problems that commonly arise, the solutions to the problems that occur and skills for solving particular problems. This process of picking the brain of an expert is a specialised form of data capture and makes use of interview techniques. The knowledge engineer is also responsible for the self consistency of the data loaded. Thus a number of specific tests have to be performed to ensure that the conclusions reached are sensible.