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Knowledge representation has two significant roles: to define a model for the AI world, and to provide a basis for reasoning techniques to get at implicit knowledge. An ordinary diagram is the set of atomic and negated atomic sentences that are true in a model. Generalized diagrams are diagrams definable by a minimal set of functions such that everything else in the model’s closure can be inferred, by a minimal set of terms defining the model. Thus providing a minimal characterization of models, and a minimal set of atomic sentences on which all other atomic sentences depend. We want to solve real world problems in AI. Obviously for automating problem solving, we need to represent the real world. Since we cannot represent all aspects of a real world problem, we need to restrict the representation to only the relevant aspects of the real world we are interested in. Let us call this subset of relevant real world aspects the Relevant World for a problem. AI approaches to problem solving represent the knowledge usually in some kind of first-order language, consisting of at least constants, functions and predicate symbols. Our primary focus will be the relations among KR, AI worlds, and the computability of models. Truth is a notion that can have dynamic properties. To keep the models, which need to be considered small and to keep a problem tractable, we have to get a grip on a minimal set of functions to define computable models with. The selector functions are applied to create compound business objects.

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