Requirements of ES
Definition
An Expert System is a knowledge-based computer program that uses stored facts, rules, and reasoning methods to provide expert-level solutions in a limited domain.
The requirements of ES are the necessary knowledge, components, resources, and design features needed for the system to function intelligently, reliably, and efficiently.
Main Content
1. Knowledge Requirement
Domain-specific knowledge
- An expert system must contain detailed knowledge about a particular subject area. This knowledge is usually collected from human experts, textbooks, case studies, manuals, and real-world experience. For example, a medical expert system needs symptoms, diseases, treatment rules, and diagnostic relationships.
Structured representation of knowledge
- The knowledge cannot be stored randomly. It must be arranged in a usable form such as rules, frames, semantic networks, decision trees, or ontologies. A common form is IF-THEN rules. Example:
IF fever AND cough THEN possible flu.
A strong knowledge base is the most important requirement of ES because the quality of the system depends on the quality of knowledge inside it. If the knowledge is incomplete or outdated, the system’s advice will also be weak or wrong.
2. Reasoning Requirement
Inference capability
- The system must be able to reason from the available facts and knowledge to reach a conclusion. This is done by an inference engine, which applies logic to the knowledge base.
Forward and backward reasoning
- Expert systems often need both data-driven reasoning and goal-driven reasoning. Forward chaining starts from known facts and moves toward conclusions. Backward chaining starts from a goal or hypothesis and checks whether the facts support it.
Reasoning is what makes an expert system “intelligent.” Without a reasoning mechanism, the system would only store information and could not solve problems. For example, in a troubleshooting system, if a printer is not working, the system may ask questions, test conditions, and infer the likely fault step by step.
3. User Interaction and Explanation Requirement
User-friendly interface
- The system must communicate clearly with users. Since many users are non-experts, the interface should be simple, direct, and easy to understand. It may use menus, forms, natural language, or chatbot-style interaction.
Explanation facility
- A good expert system should explain how it reached a conclusion and why it asked a specific question. This helps users trust the system and verify its logic. For example, the system may say, “I concluded this because the patient has high fever, sore throat, and swollen glands.”
This requirement is very important in practical applications. People are more likely to accept the system’s advice when they can see the reasoning behind it. It also helps in learning, debugging, and improving the system.
Working / Process
1. Knowledge acquisition
- Experts, documents, and databases are studied to collect relevant facts, rules, and procedures.
2. Knowledge representation and inference setup
- The collected knowledge is organized into a knowledge base and connected to a reasoning engine that can apply logical rules.
3. Interaction and conclusion generation
- The user provides facts or answers questions, the system processes them using its rules, and then produces a conclusion, recommendation, or explanation.
Advantages / Applications
Consistent decision-making
- An expert system gives the same type of answer for the same inputs, reducing human inconsistency and fatigue.
24/7 availability
- It can provide support anytime without needing human experts to be present.
Useful in many fields
- Expert systems are widely used in medical diagnosis, engineering fault detection, financial advice, customer service, legal guidance, and industrial monitoring.
Summary
- Requirements of ES are the essential knowledge, reasoning, and interaction features needed for an expert system to work properly.
- The system must have correct domain knowledge, logical inference, and a clear explanation facility.
- In simple words, an expert system works well only when it has the right knowledge, the right reasoning method, and a good way to communicate with the user.
- Important terms to remember: knowledge base, inference engine, forward chaining, backward chaining, explanation facility, domain knowledge.