An Expert System is a computer-based AI system designed to simulate human expertise in a particular field.
- It is used for problem-solving and decision-making in areas like medical diagnosis, engineering, and finance.
Components of Expert Systems:
1.) Knowledge Base:
The Knowledge Base is a collection of facts, rules, and heuristics that represent domain-specific expertise.
- The Knowledge Base is continuously updated with new data and expert insights.
- Example: A medical expert system stores diseases, symptoms, and treatments.
2.) Inference Engine:
The Inference Engine is the reasoning mechanism that applies logical rules to the knowledge base to draw conclusions and make decisions based on user input.
- It evaluates user input, applies rules from the knowledge base, and generates responses.
- It uses two types of reasoning: Forward Chaining and Backward Chaining.
- Example: Diagnosing a patient based on symptoms provided.
3.) User Interface:
The User Interface allows users to interact with the expert system by inputting data and receiving explanations.
- The UI ensures that non-experts can easily ask questions, input data, and understand system recommendations.
- It can be text-based (command-line), graphical (GUIs), or voice-based (chatbots, virtual assistants).
4.) Working Memory:
The Working Memory is a temporary storage space that holds data and intermediate results while the system processes a query.
- It stores facts and data related to the current problem, ensuring smooth decision-making.
- It holds user input and intermediate steps used by the Inference Engine before reaching a conclusion.
- Once a case is solved, the Working Memory is cleared to process new queries.
Development of Expert Systems:
Developing an Expert System involves several key steps, ensuring that the system can accurately capture human expertise, process logical reasoning, and interact with users.
The process includes:
- Knowledge Acquisition: The process of collecting knowledge from human experts, books, research papers, and databases.
- Knowledge Representation: The process of structuring and organizing acquired knowledge in a way the system can process.
- Inference Mechanism Development: The development of reasoning techniques that allow the system to apply knowledge to solve problems.
- User Interface Design: Creating an interface that allows users to interact with the expert system easily.
- Testing & Validation: Evaluating the system’s accuracy, reliability, and performance before deployment.
