Expert System Architecture Part 4

13.6 Distinguishing Features of Expert Systems

Quick Availability and Opportunity to Program Itself

Expert systems are created faster using everyday language for the rule base (with an untouchable engine) than conventional programs. Users and experts can create them without the need for professional developers or explanations of the subject.

The capacity to utilize a significant amount of information.

The expert system operates differently from traditional programs, utilising a rule base. This means that the amount of knowledge within the program is not a major factor. It functions in the exact same way whether there are ten rules or 10,000 within the rule base.

Reliability

The dependability of an expert system is equivalent to that of a database, which is considered to be sturdy and more dependable than a typical program. The size of the knowledge base also plays a crucial role in determining the system’s reliability.

Scalability

To develop an expert system, one can add, modify, or delete rules. Since the rules are written in plain language, it is simple to identify which ones need to be changed or removed.

Pedagogy

Engines powered by logical reasoning can provide users with clear explanations of why they ask certain questions and how they arrived at their deductions. By doing so, they demonstrate their understanding of the expert system’s knowledge. This allows users to gain contextual knowledge and understand the system’s deductions step by step. As a result, users can have a better understanding of their problem even before the expert system provides its final answer.

Preservation and Improvement of Knowledge

Important expertise can be lost when an expert passes away, leaves their job, or retires. However, if it is recorded in an expert system, it can last forever. Building an expert system requires talking to the expert and teaching the system about their knowledge. This process helps the system reflect and improve upon their expertise.

New Areas Neglected by Conventional Computing

When developers automate vast knowledge, they may face the classic problem of “combinatorial explosion,” also known as “information overload.” This problem can lead to complex and time-consuming programs. However, reasoning expert systems don’t face this issue because the engine automatically loads combinatorics between rules. This ability can be useful for highly interactive or conversational applications, fault diagnosis, decision support in complex systems, educational software, logic simulation of machines or systems, and constantly changing software.

13.6.1 Utility of Expert Systems

Organizations seeking to resolve complex issues that require specialized knowledge in a specific field often rely on expert systems. These advanced computer programs harness artificial intelligence techniques to solve a wide spectrum of problems. One of the earliest applications of expert systems was MYCIN, which was designed to aid doctors in diagnosing and treating bacterial infections. Expert systems are also employed to evaluate geophysical data when searching for metal and petroleum deposits, as well as in industries such as banking, investments, and telecommunications. Furthermore, these systems play a critical role in robotics, natural language processing, theorem proving, and the intelligent retrieval of information from databases. The versatility of expert systems has led to their use in various other practical human endeavours.

.Notes Rule-based systems have been utilized in various applications such as traffic monitoring and control, flight system development, and budget preparation by the federal government.

An expert system that operates on rules is designed with a clear separation between its knowledge base and the part of the system that executes those rules, known as the expert system shell. The shell is not concerned with the specific practices it carries out. This separation is critical because the expert system shell can be utilized for many different issues without requiring significant modifications.

This means that making changes to the rules of an expert system can alter the program’s behavior without impacting the controlling component, which is the system shell.

When creating a rule, the language used is closely related to the language used by subject matter experts to describe solutions to problems. By composing a practice using this language, the subject matter expert is simultaneously creating a written record of problem knowledge that can be shared with others. This means that creating a rule serves two purposes; it adds functionality or changes program behavior and records crucial information about the problem domain in a human-readable form. These systems capture and maintain knowledge, ensuring the continuity of operations even as subject matter experts retire or transfer, such as mathematicians, accountants, or physicians.

In addition, experts in the field can modify the knowledge base without relying on programmers, which lowers the expenses of software upkeep and guarantees that modifications are executed as planned. These experts can integrate rules into the knowledge base through text or graphic editors that are an essential part of the system shell.

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