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Article Dans Une Revue Philosophical Transactions of the Royal Society B: Biological Sciences Année : 2003

A grounded theory of Abstraction in Artificial Intelligence

Résumé

In artificial intelligence, abstraction is commonly used to account for the use of various levels of details in a given representation language or the ability to change from one level to another while preserving useful properties. Abstraction has been mainly studied in problem solving, theorem proving, knowledge representation (in particular for spatial and temporal reasoning) and machine learning. In such contexts, abstraction is defined as a mapping between formalisms that reduces the computational complexity of the task at stake. By analysing the notion of abstraction from an information quantity point of view, we pinpoint the differences and the complementary role of reformulation and abstraction in any representation change. We contribute to extending the existing semantic theories of abstraction to be grounded on perception, where the notion of information quantity is easier to characterize formally. In the author{\textquoteright}s view, abstraction is best represented using abstraction operators, as they provide semantics for classifying different abstractions and support the automation of representation changes. The usefulness of a grounded theory of abstraction in the cartography domain is illustrated. Finally, the importance of explicitly representing abstraction for designing more autonomous and adaptive systems is discussed.

Dates et versions

hal-01176891 , version 1 (16-07-2015)

Identifiants

Citer

Jean-Daniel Zucker. A grounded theory of Abstraction in Artificial Intelligence. Philosophical Transactions of the Royal Society B: Biological Sciences, 2003, 358 (1435), pp.1293-1309. ⟨10.1098/rstb.2003.1308⟩. ⟨hal-01176891⟩
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