Last Updated: 2/6/2023
Expert Systems
What is Expert Systems?
Expert Systems are computer-based systems designed to provide expert-level guidance and support in solving complex problems in a specific domain. They are often referred to as "knowledge-based systems" because they use a knowledge base, an inference engine, and a set of heuristics to solve a problem.
An expert system has three main components:
- A knowledge base. The knowledge base contains information about a specific domain, such as medical diagnoses or stock market trends.
- An inference engine. The inference engine is the "brain" of the expert system; it uses the knowledge from the knowledge base to make decisions and provide explanations.
- A user interface. The user interface is what the end user interacts with, through which the user can input data and receive suggestions or explanations from the expert system.
How Expert Systems Work
Expert systems work by representing the knowledge of an expert in a particular field and using this knowledge to solve problems that would otherwise require human expertise. This knowledge is stored in the form of a knowledge base, which is a database of information that the expert system uses to solve problems. The knowledge base contains facts, rules, and relationships that describe the domain of expertise.
The inference engine is the core component of the expert system and is responsible for using the knowledge base to make inferences and arrive at a solution. It uses a set of heuristics or strategies to make decisions and determine which rules should be applied in solving a problem. The heuristics are designed to simulate the reasoning process of a human expert.
Classifications of Expert Systems
Expert systems can be classified based on their domain of expertise, the way they represent knowledge, and the inference engine they use.
Based on the way they represent knowledge, expert systems can be classified into:
- Rule-based expert systems: use a set of rules to represent knowledge.
- Frame-based expert systems: use a set of frames or objects to represent knowledge.
- Case-based expert systems: use a set of cases or examples to represent knowledge.
Based on the type of inference engine, expert systems can be classified into:
- Forward-chaining expert systems: these systems start from a given set of facts and use rules to make inferences.
- Backward-chaining expert systems: these systems start from a desired conclusion and work backward to find the necessary facts.
Expert Systems in Real Life
Expert systems are being used in various industries and applications today. Some examples include:
- Medical diagnosis: AI expert systems are being used in the medical field to assist doctors in diagnosing diseases and conditions. They can analyze patient data and provide a list of possible diagnoses and the likelihood of each one.
- Financial analysis: AI expert systems are used in the financial industry to analyze stock market trends and make investment recommendations. They can also detect fraud and analyze credit risk.
- Legal reasoning: AI expert systems are used in the legal field to assist lawyers in researching case law and identifying relevant precedents.
- Manufacturing: AI expert systems monitor production processes and predict maintenance needs. They can also optimize production schedules and improve efficiency.
- Transportation: AI expert systems optimize logistics and improve fleet management.
- Customer service: AI expert systems assist customer service representatives in answering customer queries and providing solutions to problems.
- Cybersecurity: AI expert systems detect and prevent cyber-attacks and analyze network traffic for signs of malicious activity.