Expert Systems Technology

 

Expert Systems Technology

Expert systems are a branch of Artificial Intelligence (AI) designed to mimic the decision-making ability of human experts. They use knowledge and inference rules to solve complex problems in a specific domain, similar to how a human specialist would.

Key Components of Expert Systems

1. Knowledge Base

   • Contains domain-specific facts, data, and rules collected from human experts.

   • Example: “If a patient has a high fever and cough, then the patient may have the flu.”

2. Inference Engine

   • Acts as the “brain” of the system.

   • Applies logical rules to the knowledge base to deduce new information or reach conclusions.

   • Two reasoning methods:

     • Forward chaining: Starts with known facts → applies rules → reaches a conclusion.

     • Backward chaining: Starts with a hypothesis → works backward to find supporting facts.

3. User Interface

   • Allows users (often non-experts) to interact with the system by entering data and receiving solutions or recommendations.

4. Explanation Facility

   • Explains the reasoning process — why certain conclusions or recommendations were made.

5. Knowledge Acquisition Module

   • Helps build or update the knowledge base, often by interviewing human experts or integrating data from other systems.

How Expert Systems Work (Step-by-Step)

1. User inputs a problem or query.

2. The inference engine checks the knowledge base for relevant rules and facts.

3. It applies reasoning (forward or backward chaining) to derive conclusions.

4. The user interface displays the solution, along with explanations if needed.

Applications of Expert Systems

• 🏥 Healthcare – Diagnosis support (e.g., MYCIN for bacterial infections).

• ⚖️ Legal – Legal reasoning and document analysis.

• 🏭 Manufacturing – Fault diagnosis, process control.

• 💰 Finance – Loan approval systems, investment advice.

• 🌾 Agriculture – Pest control recommendations, crop planning.

• 🧪 Engineering – Design analysis, equipment troubleshooting.

Advantages

• Captures and preserves human expertise.

• Provides consistent solutions.

• Can work continuously without fatigue.

• Speeds up decision-making.

• Useful where human experts are scarce.

Limitations

• Expensive and time-consuming to build and maintain.

• Limited to specific domains (no general intelligence).

• Difficulty in updating when knowledge changes rapidly.

• Cannot handle ambiguous or incomplete information as flexibly as humans.

Examples of Expert Systems

• MYCIN – Medical diagnosis system for infections.

• DENDRAL – Chemical analysis for molecular structures.

• XCON (DEC) – Computer configuration for hardware.

• CLIPS – A widely used tool for building expert systems.