Expert systems: How they use knowledge to make decisions
Imagine if a computer could act like a human expert, making complex decisions or providing informed recommendations – based on a variety of information. That’s exactly what expert systems do. They belong to the oldest yet most fundamental approaches to Artificial Intelligence (AI) and are now applied in many fields, from medicine to engineering.
In this article, you will learn what expert systems are, how they work, and why they remain indispensable in the modern AI world.
What are expert systems?
Definition
An expert system is an AI program that is based on a knowledge base and a reasoning mechanism to solve problems or make decisions. It mimics the thinking process of a human expert by combining specific knowledge and logical conclusions.
Key features of expert systems
Knowledge base: Contains specific facts and rules from a particular domain.
Reasoning mechanism: Uses these rules to make logical decisions or provide recommendations.
User interface: Facilitates interaction between the system and the user.
Examples
Medical diagnosis systems: Assisting doctors in diagnosing diseases.
Legal expert systems: Analyzing contracts or legal documents.
How do expert systems work?
Expert systems operate in three central steps:
Knowledge acquisition
The knowledge of a human expert is stored in a knowledge base. This is done through interviews, professional literature, or data analysis.
Reasoning
A reasoning mechanism utilizes the stored rules and facts to generate answers or recommendations.
Rule-based: “If-Then” rules (e.g., “If temperature > 38°C, then fever”).
Logic-based: Uses logical deductions to infer new insights from known facts.
Interaction with the user
The system asks questions or provides results in understandable form to assist the user in decision-making.
Types of expert systems
Rule-based systems
These systems use predefined rules to make decisions.
Example: Diagnosis systems that link symptoms to diseases.
Case-based systems
They learn from past cases and apply this knowledge to new problems.
Example: Systems that analyze legal precedents.
Hybrid systems
Combine rule-based approaches with machine learning or other AI methods to be more flexible and powerful.
Why are expert systems relevant?
Helping with complex decisions
Expert systems support decisions in areas where mistakes can have serious consequences, such as in medicine or finance.
Efficiency improvement
By automating tasks, they save time and resources by taking over repetitive tasks.
Access to expert knowledge
Even without direct access to a human expert, a system can provide well-informed knowledge.
Consistency
Unlike humans, expert systems always make the same decisions based on the same rules.
Applications of expert systems
Medicine
Example: Systems like MYCIN assist doctors in diagnosing infections.
Law
Example: Analysis of contracts or laws to identify legal risks.
Engineering and technology
Example: Diagnosing machines, problems, or assisting in the planning of complex systems.
Finance
Example: Identifying investment risks or fraud attempts.
Customer service
Example: Rule-based chatbots that can answer frequent customer inquiries.
Advantages of expert systems
Accessibility of expert knowledge
Even non-experts can benefit from extensive specialized knowledge without needing to consult experts.
Time savings
Decisions or analyses can be made in seconds, which would take humans hours.
Cost efficiency
Expert systems often eliminate the need for expensive human experts.
Learning ability
Modern expert systems can learn from new data through integration with machine learning.
Challenges of expert systems
Limited flexibility
Rule-based systems reach their limits when confronted with unknown or unusual scenarios.
Dependency on the knowledge base
If the knowledge base is incomplete or outdated, the system can produce incorrect results.
High development effort
The creation and maintenance of an expert system require significant time and resources, especially for complex applications.
Explainability
In some cases, it can be challenging to present the decision logic of the system in an understandable way for users.
Practical examples
MYCIN
One of the first expert systems, developed for diagnosing and treating bacterial infections.
DENDRAL
An expert system for chemical analysis that aids scientists in identifying molecular structures.
XCON
A system for configuring computer hardware that has improved efficiency in planning and ordering.
Drone control
Modern expert systems analyze flight schedules and sensor data to operate drones efficiently.
The future of expert systems
Integration with machine learning
Hybrid systems combining expert systems with learning AI models will become more adaptable and powerful.
New applications
Expert systems could play a key role in areas like environmental management or smart cities.
Improved explainability
Future systems may articulate their decisions more clearly and transparently to build user trust.
Scalability and automation
Cloud-based expert systems could be made accessible worldwide to democratize knowledge and expertise.
Conclusion
Expert systems are a proven technology for leveraging knowledge and efficiently supporting decisions. They offer immense advantages, particularly in complex and critical areas such as medicine, law, and engineering.
Despite some challenges, expert systems remain a valuable tool that is becoming increasingly powerful through advancements in Artificial Intelligence. If you want to automate decisions in your business or project, an expert system may be the right solution for you.