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Knowledge Representation & Reasoning

September 4th, 2010 No comments

Knowledge representation and reasoning is an area of artificial intelligence whose fundamental goal is to represent knowledge in a manner that facilitates inferencing (i.e. drawing conclusions) from knowledge. It analyzes how to formally think – how to use a symbol system to represent a domain of discourse (that which can be talked about), along with functions that allow inference (formalized reasoning) about the objects. Some kind of logic is used to both supply formal semantics of how reasoning functions apply to symbols in the domain of discourse, as well as to supply operators such as quantifiers, modal operators, etc. that, along with an interpretation theory, give meaning to the sentences in the logic.

When we design a knowledge representation (and a knowledge representation system to interpret sentences in the logic in order to derive inferences from them), we have to make choices across a number of design spaces. The single most important decision to be made is the expressivity of the KR. The more expressive, the easier and more compact it is to “say something”. However, more languages that are expressive are harder to automatically derive inferences from. An example of a less expressive KR would be propositional logic. An example of a more expressive KR would be auto epistemic temporal modal logic. Less expressive KRs may be both complete and consistent (formally less expressive than set theory). KRs that are more expressive may be neither complete nor consistent.