Types of Expert System in AI

➺ An expert system is a computer program designed to simulate the problem-solving behavior of a human expert in a particular domain. It uses knowledge and reasoning techniques to provide intelligent advice or decision-making support for specific problems.

➺ There are several types of expert systems:

➺ Rule-based Expert Systems: These systems use a set of "if-then" rules to make decisions based on input data. The rules are created by human experts in a particular domain and encoded into the system. When new data is inputted, the system evaluates the rules and generates an output.

➺ Case-based Expert Systems: These systems use a knowledge base of past cases to solve new problems. When presented with a new problem, the system searches the knowledge base for similar cases and adapts the solution accordingly.

➺ Fuzzy Logic Expert Systems: This type of system uses fuzzy logic to represent and manipulate uncertain or vague information. which allows for degrees of truth instead of just true or false.

➺ Neural Network Expert Systems: Neural networks are a type of machine learning that learns from examples. These systems can be used to recognize patterns and make decisions based on them.

➺ Bayesian Expert Systems: These systems use probability theory to make decisions based on uncertain information. The system represents the domain knowledge as a graph of nodes and uses conditional probabilities to update the belief in each node given new evidence.

➺ Case-based reasoning systems: These systems use a database of past cases to solve new problems. The system retrieves a similar past case and adapts it to the new problem.

➺ Hybrid systems: These are expert systems that combine two or more of the above types of systems. For example, a hybrid system may combine a rule-based system with a neural network.

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