Expert Systems
Expert Systems
Key Definitions
- An Expert System is a computer system built to emulate the decision-making abilities of a human expert.
- It is based on the fields of artificial intelligence (AI) and cognitive science, having the capacity to make suggestions, predictions, and solve complex problems.
- These systems use extensive databases of knowledge and a set of rules for applying the knowledge base to each given situation.
Types of Expert Systems
- Diagnosis Systems are medical expert systems that assist doctors in diagnosing diseases by analysing symptoms and patient history.
- Prescription Systems suggest appropriate treatments or drugs based on the diagnosed disease and patient’s medical history.
- Process Control Systems monitor and control complex industrial processes, predicting potential issues, scheduling maintenance and controlling production.
- Forecasting Systems use historical data and trends to make predictions about future outcomes in fields such as weather prediction, stock market analysis, and business projections.
Usage of Expert Systems
- Diagnosis Systems are often employed in hospitals to provide additional support to healthcare professionals.
- Prescription Systems are used in pharmacies, hospitals, and other healthcare institutions to ensure the correct treatment is administered.
- Process Control Systems are used in industries such as oil, gas, and nuclear power to ensure safe and efficient operation.
- Forecasting Systems are valuable in finance, business planning, and meteorology to make informed decisions and predictions.
Advantages of Expert Systems
- Expert systems can work relentlessly around the clock, providing consistent results without fatigue affecting their performance.
- They can store more information than a human expert, and can recall this information instantly when required.
- They enable decision-making in areas where it may be too risky or costly for humans to be directly involved.
- They aid in solving complex problems by structuring them in a way that is easier for humans to understand.
Potential Drawbacks
- Expert systems rely on the accuracy of the information fed to them - any errors in the knowledge base may lead to incorrect results.
- They lack the common sense, intuition, and creativity that a human expert might possess.
- Implementation of an expert system can be time-consuming and expensive.
- Regular updating and maintenance of the system’s knowledge base and rules are needed, which further increases costs.
Real-World Examples
- Diagnosis Systems like IBM’s Watson for Health, assist physicians in diagnosing diseases by analysing medical reports.
- Prescription Systems such as DSS Inc’s Juno EHR, guide medical practitioners on the best course of treatment for a particular patient.
- Process Control Systems applications are found in Tesla’s Gigafactory, which uses expert systems for optimizing battery production.
- Forecasting Systems are employed by national weather services and meteorological departments, financial institutions like Bloomberg and many large corporations for strategic planning.