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This chapter describes a nonmonotonic causal logic designed for representing knowledge about the effects of actions.
We also outline the main methods used by these algorithms for representing knowledge about shape, identity, and relationships.
We then introduce representational structures and a common vocabulary for representing knowledge about robot actions, events, objects, environments, and the robot's hardware as well as inference procedures that operate on this common representation.
We introduce the idea of a "state-inference function" (from observation sequences to underlying hidden states) for representing knowledge about a process and develop an efficient sequential-inference algorithm, utilizing this function, that is correct for processes that generate reliable observations consistent with the state-inference function.
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As Dan says in response: "Clearly the brain represents knowledge about red differently from how it actually processes visual signals.
A core question in cognitive science concerns how humans acquire and represent knowledge about their environments.
Domain reference ontologies represent knowledge about a particular part of the world in a way that is independent from specific objectives, through a theory of the domain.
This work describes interoperability issues in the engineering process of the OntoAgroHidro, an ontology that represents knowledge about impacts of agricultural activities and climatic changes on water resources.
With the goal of helping teachers in planning collaborative scenarios, we have developed an intelligent authoring tool referred to as CHOCOLATO using Semantic Web technologies (e.g. ontologies) in order to represent knowledge about different pedagogies and practices related to collaboration.
OPM is a systems-modeling approach that represents knowledge about systems concurrently and bi-modally through graphics (a set of Object-Process Diagrams, OPDs) and text (Object-Process Language, OPL, a subset of English), yielding a single, unified and consistent view.
Ontologies [ 16] can structure and represent knowledge about a certain domain in a formal way.
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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