Expert systems in AI are made to mimic human experts’ decision-making processes. Their application areas and underlying technologies are used to categorize them in various types. Here are the primary types of expert systems in AI:

1. Rule-Based Expert Systems

Rule-based expert systems are the most common type of expert system. They rely on a set of if-then rules to make decisions. The system applies these rules to the input data to reach conclusions. This type of system is effective in structured environments where clear, logical rules can be applied to problem-solving.

Example: In a healthcare expert system, a rule might be: If a patient has a fever and sore throat, then consider a diagnosis of strep throat.

2. Frame-Based Expert Systems

Frame-based expert systems use data structures called frames to represent knowledge. Each frame contains attributes and associated values, allowing the system to make decisions based on the current state of the attributes. This approach is useful when dealing with complex data sets where relationships between data points need to be understood.

Example: A frame-based system for vehicle maintenance might have frames like engine condition, oil level, and tire pressure, each with predefined values. When new data is input, the system compares it to the existing frames and makes recommendations.

3. Fuzzy Logic Systems

Fuzzy logic systems handle uncertainty by allowing degrees of truth rather than binary true/false decisions. This enables expert systems to make more nuanced decisions in ambiguous or uncertain situations.

Example: In climate control systems, fuzzy logic can manage temperature settings more flexibly based on partial data.

4. Neural Network-Based Expert Systems

These systems combine neural networks with rule-based logic. Neural networks enable the system to learn from data and improve its decision-making capabilities over time. 

Example: Expert systems used in financial trading leverage neural networks to predict market trends based on historical data and predefined rules.

5. Neuro-Fuzzy Expert Systems

Neuro-fuzzy systems integrate the learning capabilities of neural networks with the handling of uncertainty in fuzzy logic systems. These hybrid systems are especially useful in fields like engineering, where both precision and adaptability are required.