Characteristics of an Expert System

CHARACTERISTICS OF AN EXPERT SYSTEM

The growth of expert system is expected to continue for several years. With the continuing growth, many new and exciting applications will emerge. An expert system operates as an interactive system that responds to questions, asks for clarification, makes recommendations and generally aids the decision making process. Expert system provides expert advice and guidance in a wide variety of activities from computer diagnosis to delicate medical surgery.

An expert system is usually designed to have the following general characteristics.

1.     High level Performance: The system must be capable of responding at a level of competency equal to or better than an expert system in the field. The quality of the advice given by the system should be in a high level integrity and for which the performance ratio should be also very high.

2.     Domain Specificity: Expert systems are typically very domain specific. For ex., a diagnostic expert system for troubleshooting computers must actually perform all the necessary data manipulation as a human expert would. The developer of such a system must limit his or her scope of the system to just what is needed to solve the target problem. Special tools or programming languages are often needed to accomplish the specific objectives of the system.

3.     Good Reliability: The expert system must be as reliable as a human expert.

4.     Understandable: The system should be understandable i.e. be able to explain the steps of reasoning while executing. The expert system should have an explanation capability similar to the reasoning ability of human experts.

5.     Adequate Response time: The system should be designed in such a way that it is able to perform within a small amount of time, comparable to or better than the time taken by a human expert to reach at a decision point. An expert system that takes a year to reach a decision compared to a human expert’s time of one hour would not be useful.

6.     Use symbolic representations: Expert system use symbolic representations for knowledge (rules, networks or frames) and perform their inference through symbolic computations that closely resemble manipulations of natural language.

7.     Linked with Metaknowledge: Expert systems often reason with metaknowledge i.e. they reason with knowledge about themselves and their own knowledge limits and capabilities. The use of metaknowledge is quite interactive and simple for various data representations.

8.     Expertise knowledge: Real experts not only produce good solutions but also find them quickly. So, an expert system must be skillful in applying its knowledge to produce solutions both efficiently and effectively by using the intelligence human experts.

9.     Justified Reasoning: This allows the users to ask the expert system to justify the solution or advice provided by it. Normally, expert systems justify their answers or advice by explaining their reasoning. If a system is a rule based system, it provides to the user all the rules and facts it has used to achieve its answer.

10.            Explaining capability: Expert systems are capable of explaining how a particular conclusion was reached and why requested information is needed during a consultation. This is very important as it gives the user a chance to access and understand the system’s reasoning ability, thereby improving the user’s confidence in the system.

11.            Special Programming Languages: Expert systems are typically written in special programming languages. The use of languages like LISP and PROLOG in the development of an expert system simplifies the coding process. The major advantage of these languages, as compared to conventional programming languages is the simplicity of the addition, elimination or substitution of new rules and memory management capabilities. Some of the distinguishing characteristics of programming languages needed for expert system work are as follows:

            Efficient mix of integer and real variables.

            Good memory management procedures.

            Extensive data manipulation routines.

            Incremental compilation.

            Tagged memory architecture.

            Efficient search procedures.

            Optimization of the systems environment.