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By efficiently recalling similar past instances of problem solving and their contexts, intelligent agents are immediately provided with implicit knowledge about relevance, encoded in the retrieved contexts and, in the same time, with more possibilities to reduce the instantiation uncertainty of general knowledge when applied to specific problems.
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The knowledge gap translates into the instantiation uncertainty that characterizes the application of general knowledge to specific problems.
The knowledge gap and inherent instantiation uncertainty manifested in the application of general knowledge does not fully enable the education of providers and patients which would require additional knowledge about individual contexts of successful or unsuccessful application instances.
For example, knowing generally that a certain drug may give allergic reactions but being uncertain whether a particular patient may or may not develop any, is an example of what we consider the uncertainty associated with general knowledge.
This knowledge gap translates into uncertainty when a general knowledge fact is instantiated to a specific situation.
Given a limited warning time, an asteroid impact mitigation campaign would hinge on uncertainty-based information consisting of remote observational data of the identified Earth-threatening object, general knowledge of near-Earth asteroids (NEAs), and engineering judgment.
A general knowledge of meteorology is assumed.
Teacher certification requires just a general knowledge of reading.
Test your general knowledge of Coupling.
You talked about general knowledge of roofs, doors and windows.
General knowledge of fungal pathogens is assumed.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com