Automated Inference System

AKA: Reasoning Engine.
Context:
- It can apply an Inference Rule.
- It can include a Predictive Model.
- It can include a Business Logic Model.
- It can range from being a Production Automated Inference System to being ...
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Example(s):
- a Rule-based Reasoning Engine.
  - Jess.
  - iFact.
  - Racer.
- an LLM-based Reasoning Engine.
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Counter-Example(s):
- Knowledge Base System.
- Model Training System.
See: Expert System, Production Computing System, Logic System, Backward Chaining, Forward Chaining, Expert Systems, Knowledge Base System.

References

(Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/inference_engine Retrieved:2019-11-15.
- In the field of Artificial Intelligence, inference engine is a component of the system that applies logical rules to the knowledge base to deduce new information. The first inference engines were components of expert systems. The typical expert system consisted of a knowledge base and an inference engine. The knowledge base stored facts about the world. The inference engine applies logical rules to the knowledge base and deduced new knowledge. This process would iterate as each new fact in the knowledge base could trigger additional rules in the inference engine. Inference engines work primarily in one of two modes either special rule or facts: forward chaining and backward chaining. Forward chaining starts with the known facts and asserts new facts. Backward chaining starts with goals, and works backward to determine what facts must be asserted so that the goals can be achieved.