An expert system is a computer program that is designed to solve complex problems and to provide decision-making ability like a human expert. It performs this by extracting knowledge from its knowledge base using the reasoning and inference rules according to the user queries.
Below is the block diagram that represents the working of an expert system:
Role of Inference Engine:
- The inference engine is known as the brain of the expert system as it is the main processing unit of the system. It applies inference rules to the knowledge base to derive a conclusion or deduce new information. It helps in deriving an error-free solution to queries asked by the user.
- With the help of an inference engine, the system extracts knowledge from the knowledge base.
- There are two types of inference engines:
- Deterministic Inference engine: The conclusions drawn from this type of inference engine are assumed to be true. It is based on facts and rules.
- Probabilistic Inference engine: This type of inference engine contains uncertainty in conclusions, and is based on probability.
The inference engine uses the below modes to derive the solutions:
- Forward Chaining: It starts from the known facts and rules, and applies the inference rules to add their conclusion to the known facts.
- Backward Chaining: It is a backward reasoning method that starts from the goal and works backward to prove the known facts.