Knowledge representation and reasoning

Knowledge representation (KR) aims to model information in a structured manner to formally represent it as knowledge in knowledge-based systems. Whereas knowledge representation and reasoning (KRR, KR&R, or KR²) also aims to understand, reason and interpret knowledge. KRR is widely used in the field of artificial intelligence (AI) with the goal to represent information about the world in a form that a computer system can use to solve complex tasks, such as diagnosing a medical condition or having a natural-language dialog. KR incorporates findings from psychology^[1] about how humans solve problems and represent knowledge, in order to design formalisms that make complex systems easier to design and build. KRR also incorporates findings from logic to automate various kinds of reasoning.

Examples of knowledge representation formalisms include vocabularies, thesaurus, semantic networks, axiom systems, frames, rules, logic programs, and ontologies. Examples of automated reasoning engines include inference engines, theorem provers, model generators, and classifiers.