Structure of an Expert System

STRUCTURE OF AN EXPERT SYSTEM

The structure of expert systems reflect the knowledge engineers understanding of the methods of representing knowledge and of how to perform intelligent decision making tasks with the support of a computer based system. Complex decisions involve intricate combination of factual and heuristic knowledge. In order for the computer to be able to retrieve and effectively use heuristic knowledge, the knowledge must be organized in an easily accessible format that distinguishes among data, knowledge and control structures. For this reason expert systems are organized in three distinct levels like:

(a) Knowledge Base: It consists of problem solving rules, procedures and intrinsic data relevant to the problem domain. The knowledge base constitutes the problem solving rules, facts or intuition that a human expert might use in solving problems in a given problem domain. The knowledge base is usually stored in terms of if-then rules. The working memory represents relevant data for the current problem being solved.

b)    Working Memory: It refers to task specific data for the problem under consideration. This is the dynamic module of the system. It consists of an essential component called database. In general, the workspace contains a set called rule base, i.e. it contains a set of rules that to be used by a system at a given moment.

c)     Inference Engine: This is a generic control mechanism that applies the axiomatic knowledge in the knowledge base to the task specific data to arrive at some solution or conclusion. Inference in production systems is accomplished by a process of chaining through the rules recursively, either in a

forward or in a backward direction until a conclusion is reached.

These three pieces may very well come from different sources. The inference engine, such as VP-Expert, may come from a commercial vendor. The knowledge base may be a specific diagnostic knowledge base compiled by a consulting firm, and the problem data may be supplied by the end user. A knowledge base is the nucleus of the expert system structure. A knowledge base is created by knowledge engineers, who translate the knowledge of real human experts into rules and strategies. These rules and strategies can change depending on the prevailing problem scenario. The knowledge base provides the expert system with the capability to recommend directions for user inquiry. The system also instigates further investigation into areas that may be important to a certain line of reasoning but not apparent to the user. The general structure of an expert system is given in figure .

modularity of an expert system is an important distinguishing characteristics compared to a conventional computer program. Modularity is affected in an expert system by the use of three distinct components as shown in fig 6.2. A good expert system is expected to grow as it learns from user feedback. Feedback is incorporated into the knowledge base as appropriate to make the expert system smarter. The dynamism of the application environment for expert systems is based on the individual dynamism of the components. This can be classified into three categories as follows.

a)     Most dynamic: The most dynamic part of an expert system is always the working memory. The content of the working memory, sometimes called the data structure, changes with each problem situation. Consequently, it is the most dynamic component of an expert system assuming, of course that it is kept current.

b)    Moderately dynamic: This part in the expert system is the knowledge base. The knowledge base need not change unless a new piece of information arises that indicates a change in the problem solution procedure. Changes in the knowledge base should be carefully evaluated before being implemented. In effect, changes should not be based on just one consultation experience.

c)     Least dynamic: The least dynamic part is the inference engine. As the control and coding structure of an inference engine is very strict, so changes are made only if absolutely necessary to correct a bug or enhance the inferential process. Commercial inference engines, in particular, change only at the discretion of the developer. Since frequent updates can be disruptive and costly to clients, most commercial software developers try to minimize the frequency of updates.