The inference engine is the core component of a traditional AI expert system. It is responsible for applying logical rules to the knowledge base to derive conclusions, make decisions, or solve problems. Essentially, it mimics human reasoning by analyzing known facts and drawing new insights based on predefined rules.

In an expert system, the inference engine operates in two primary modes:

1. Forward chaining – It starts with known facts and applies rules to infer new facts, moving step by step toward a conclusion. This method is typically used for problem-solving and automation, such as diagnosing diseases based on symptoms.

2. Backward chaining – It begins with a goal or hypothesis and works backward to determine what facts must be true for the conclusion to hold. This is common in troubleshooting applications, such as identifying faults in a mechanical system.

By continuously evaluating and applying rules, the inference engine ensures that the expert system can provide intelligent responses, make predictions, or offer recommendations based on structured knowledge. This makes it a crucial part of AI applications in fields like medicine, engineering, and business decision-making.

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