An inference engine is a core component of a knowledge-based system or artificial intelligence (AI) application. It is responsible for drawing conclusions or making decisions based on a set of rules and facts stored in the system’s knowledge base. Inference engines are widely used in expert systems, decision support systems, and AI applications to automate reasoning and solve complex problems.

The process of inference generally follows one of two methods: forward chaining or backward chaining. In forward chaining, the inference engine starts with available data and applies rules to infer new facts until a goal is reached. In contrast, backward chaining begins with a goal and works backward, checking if known facts and rules support it. These mechanisms enable the inference engine to deduce new insights or verify hypotheses efficiently.

Inference engines rely heavily on logic-based frameworks, such as propositional or predicate logic, to evaluate conditions and execute rules. They are also equipped to handle uncertainties using probabilistic models or fuzzy logic when exact reasoning is not possible.

Applications of inference engines span multiple domains. In medical diagnosis systems, they assist doctors by suggesting possible diseases based on symptoms and test results. In customer service, chatbots use inference engines to provide contextually relevant responses. In cybersecurity, they analyze threat patterns to recommend countermeasures.

With the growing adoption of machine learning and AI, modern inference engines are increasingly integrated with data-driven models to enhance their reasoning capabilities, enabling more robust and adaptive decision-making.