Here's a breakdown of what it does:
* Receives input: The inference engine takes input, typically in the form of facts or queries. These facts might be provided by a user, sensor data, or another system.
* Applies rules: It uses the input facts and the rules stored in the knowledge base to deduce new facts. The rules are typically "if-then" statements that describe relationships between facts.
* Derives conclusions: Through the application of rules, the inference engine chains together facts and reaches conclusions. This process of chaining can be forward chaining (starting with facts and deriving conclusions) or backward chaining (starting with a hypothesis and searching for facts to support it).
* Provides output: Finally, the inference engine presents its conclusions to the user or other system. This output might be a diagnosis, a recommendation, or a simple yes/no answer.
Types of Inference Engines:
Inference engines can be categorized based on their reasoning methods:
* Forward Chaining (Data-driven): Starts with known facts and uses rules to deduce new facts until a goal is reached or no more rules can be applied. It's suitable for situations where you have a lot of data and want to explore potential conclusions.
* Backward Chaining (Goal-driven): Starts with a hypothesis (goal) and works backward, finding facts that support or refute the hypothesis. It's efficient when you have a specific question and want to find the evidence to support or deny it.
* Hybrid approaches: Some inference engines combine forward and backward chaining to leverage the strengths of both methods.
Example:
Imagine a simple expert system for diagnosing car problems.
* Knowledge Base:
* Rule 1: IF engine won't start AND battery is dead THEN problem is dead battery.
* Rule 2: IF engine won't start AND battery is OK THEN problem is starter motor.
* Fact: Engine won't start.
* Fact: Battery is dead.
* Inference Engine (Forward Chaining): The engine would use the facts "engine won't start" and "battery is dead" to match the conditions of Rule 1, concluding "problem is dead battery."
In summary, the inference engine is the brains of an expert system, intelligently using its knowledge base to reason and solve problems. It's a crucial element in artificial intelligence and knowledge representation.
