As expert systems moved from research prototypes to deployed systems there was more focus on issues such as speed and robustness.
A knowledge-based system (KBS) is a form of artificial intelligence (AI) that aims to capture the knowledge of human experts to support decision-making. Examples of knowledge-based systems include expert systems, which are so called because of their reliance on human expertise.
A classic example of a rule-based system is the domain-specific expert system that uses rules to make deductions or choices. For example, an expert system might help a doctor choose the correct diagnosis based on a cluster of symptoms, or select tactical moves to play a game.
The inference engine, allows new knowledge to be inferred.
- http://www.cse.unsw.edu.au/~cs9416/01-Overview/overview.html
- https://en.wikipedia.org/wiki/Inference_engine
- https://iep.utm.edu/prop-log/
- https://discrete.openmathbooks.org/dmoi2/sec_propositional.html
- https://brilliant.org/wiki/propositional-logic/ <3
- Inference Engine
- Universal Quantification
- Existential Quantification
- Semantic Reasoner : A semantic reasoner, reasoning engine, rules engine, or simply a reasoner, is a piece of software able to infer logical consequences from a set of asserted facts or axioms (https://en.wikipedia.org/wiki/Semantic_reasoner)
- Inductive Inference/Reasoning
- Forward Chaining - https://en.wikipedia.org/wiki/Forward_chaining
- Backward Chaining
- Truth Table
- Domain Specific Language - https://martinfowler.com/bliki/BusinessReadableDSL.html
- Passing fact as reference into the working memory.
- Memory requirements:
- Storing facts
- Inference process
- Persistence:
- Entity Attribute Value (EAV)
- Geometric and Topological Inference
- CLIPS - https://www.clipsrules.net/
- Dealing with properties by Martin Fawler - https://martinfowler.com/apsupp/properties.pdf
- Fixed Property
- Dynamic Property
- Flexible Dynamic Property (string as parameter)
- Typed Dynamic Property
- Separate Properties
- Dynamic Property Knowledge Level