An Expert system is a computer program that uses artificial intelligence to replicate the decision-making abilities of a human expert in a specific field.
Purpose of an Expert System
- Solve complex problems
- Expert systems are designed to handle complex problems that usually require human expertise
- Enhance decision-making
- They assist in making informed decisions by providing accurate and reliable recommendations based on the available data
- Save time and resources
- Expert systems can process vast amounts of data quickly, reducing the time and effort required by human experts
- Consistency and accuracy
- They ensure consistent and accurate results by eliminating human error and bias
- Knowledge preservation
- Expert systems store and preserve the knowledge of experts, ensuring it is not lost when the expert retires or is unavailable
Uses of Expert Systems
- Mineral prospecting
- Analyse geological data
- Identify potential locations for mineral deposits
- Car engine fault diagnosis
- Determine issues within engine components
- Suggest repair options and maintenance schedules
- Medical diagnosis
- Analyse patient symptoms and medical history
- Suggest possible diagnoses and treatment plans
- Chess games
- Evaluate possible moves based on the game state
- Plan strategic moves to increase chances of winning
- Financial planning
- Evaluate investment options and risks
- Provide personalised financial advice
- Route scheduling for delivery vehicles
- Calculate optimal routes based on factors like distance, traffic, and time constraints
- Reduce fuel consumption and improve efficiency
- Plant and animal identification
- Analyse physical characteristics and habitat data
- Identify species and provide relevant information
- Career recommendations
- Ask the user a series of questions / analyse existing qualifications
- Make recommendations on career choices
Components of an Expert System
Components of an Expert System
- User Interface:
- Allows users to interact with the expert system
- Provides a platform for inputting data and receiving recommendations or solutions
- Designed for ease of use and accessibility
- Inference Engine:
- A core component of the expert system that performs logical reasoning
- Applies rules from the rules base to the data from the knowledge base
- Mimics human decision-making processes to generate conclusions
- Knowledge Base:
- Repository for domain-specific information, facts, and data
- Contains expertise gathered from human experts or other reliable sources
- Essential for the inference engine to make accurate recommendations
- Rules Base:
- Stores logical rules and relationships governing the domain
- Guides the inference engine in applying reasoning to the data
- Rules can be modified or updated as new information becomes available
- Explanation System:
- Provides transparency in the decision-making process
- Offers detailed explanations of the expert system’s reasoning and conclusions
- Enhances user trust and understanding of the system’s recommendations
How an Expert System is Used to Produce Possible Solutions
- Expert systems use the knowledge base and rules base to analyse input data
- The inference engine applies rules and logic to the input data
- The system generates potential solutions or recommendations based on the applied rules
- The explanation system communicates the reasoning behind the suggested solutions
