The Inference Engine is a critical component of an artificial intelligence system, responsible for conducting the cogitative part of the AI’s work. It uses the built-in knowledge and rules to take input data and generate output often in the form of decisions, predictions, or classifications.
Imagine a detective who uses clues and knowledge they already have to solve a mystery. That’s what an inference engine does. Given certain information, it works out answers to questions or solutions to problems using the “knowledge” it’s learned before.
In the field of artificial intelligence, an inference engine is tasked with deriving new relevant information from the knowledge it already has. This is typically done using pre-defined rules and logic. At its core, the inference engine feeds on two main components:
Knowledge Base: This is essentially the part of the system that embodies the ‘knowledge’ or ‘intelligence’. It comprises facts and heuristics that the system has gained throughout its learning phase.
Fact Base: These are the actual inputs or ‘clues’ that the system uses to work with. For example, in recognizing images, the fact base would contain features of the objects that need to be recognized.
In essence, the inference engine works in a cyclical fashion. It takes a fact from the fact base and applies the knowledge from the knowledge base. Then, the result of this operation is inserted back into the fact base. This cyclical operation of inferencing continues until a desirable goal has been achieved or no more new facts can be inferred.
It’s worth noting that the specific implementation of an inference engine may vary depending on the particular use case or the domain in which it’s applied. For example, in a rule-based system (like a decision tree), the inference engine uses a process called “unification” to apply its rules. Essentially, unification is a way of making different bits of information compatible with each other, so they can be used together.
In contrast, neural networks (and by extension, deep learning systems) use an inference engine in a slightly different way. Here, the facts are input data and the knowledge is represented as a complex interconnection of neurons with varying connection strengths (weights). The inference engine then propagates the data through this network and adjusts neuron activations based on the weights to generate the output.
Another perspective of inference comes from probabilistic models such as Bayesian Networks or Hidden Markov Models, where inference involves calculating probability distributions to infer unknown quantities.