Expert systems

Expert systems are AI software designed to replicate the decision-making ability of a human specialist in a specific domain. Unlike modern machine learning models, they do not learn from data. Instead, they rely on knowledge encoded by experts, stored in a knowledge base, and applied to problems through a reasoning engine.

The architecture consists of five core components.

• The knowledge base holds domain-specific facts and rules.
• The inference engine applies this knowledge to reach conclusions.
• The knowledge acquisition module allows the system to incorporate new information.
• The user interface enables non-experts to interact with the system.
• The explanation module clarifies how conclusions were reached.

Inference engines operate in two directions. The first is forward chaining that starts with known facts to derive outcomes—useful for prediction, such as forecasting market reactions. Then, there’s backward chaining that starts with a desired outcome and works backward to identify possible causes—useful for diagnosis, such as determining what triggered a patient’s condition.

A core limitation of expert systems is that they cannot learn autonomously and require ongoing manual updates. Also, they are confined to their specific domain and can become expensive and resource-intensive as they scale.