An inference engine is a component of an expert system that uses logical rules and reasoning to derive new information or make decisions based on existing knowledge in the knowledge base. It essentially performs the reasoning process of an expert system by using the rules and facts stored in the knowledge base to draw conclusions and answer questions.
The inference engine works by matching rules against the available facts and applying the appropriate rules to generate new facts or draw conclusions. It uses various inference techniques such as forward chaining, backward chaining, and rule-based reasoning to carry out the reasoning process.
In forward chaining, the inference engine starts with the available facts and applies rules to derive new facts until a goal is reached. Backward chaining, on the other hand, starts with the goal and works backward to find the rules and facts needed to prove the goal.
The inference engine also uses techniques such as truth maintenance systems to track the truth or falsity of facts and rules as new information is added to the knowledge base. This helps to ensure consistency and accuracy in the reasoning process.
Overall, the inference engine is a crucial component of an expert system that allows it to use the available knowledge to make decisions and solve problems.
