An inference engine a core component of expert systems in artificial intelligence is a tool used to apply reasoning capabilities to a knowledge base to derive conclusions from a set of facts or assertions. It is the brain of the expert system that applies logic rules (domain-specific expertise) to the data (facts) to answer queries, solve problems, or provide recommendations.
The inference engine uses two primary modes of reasoning: forward chaining and backward chaining. In forward chaining, the inference engine considers the available facts and applies the rules to infer more facts, continuing this process until no more new information can be derived or a specific goal is achieved. This works like a cause-effect heuristic, starting from the known data and finding what can be derived from it. Backward chaining begins with a potential conclusion or hypothesis in mind and works backward, examining the rules and facts to see whether such a conclusion can be validated. This can be likened to a goal-driven approach, starting from the desired outcome and deducing what initial data might lead to it.
The power of an inference engine lies in its ability to mimic human expert reasoning within specific domains, applying rules, laws, or heuristics to known data to process, infer and generate new data. This makes inference engines highly valuable in decision-making systems, such as medical diagnosis systems, financial planning services, troubleshooting technical systems, and in many other fields where complex decision-making is required based on sets of rules or constraints.