Logic-based knowledge representation is one of the main building blocks of (logic-based) artificial intelligence. While most successful knowledge representation languages are based on classical logic, realistic intelligent applications need to handle uncertainty in an adequate manner. Over the years, many different languages for representing uncertain knowledge—often extensions of classical knowledge representation languages—have been proposed. We briefly present some of the defining properties of these languages as they pertain to the family of probabilistic description logics. This limited view is intended to help pave the way for the interested researcher to find the most adequate language for their needs, and potentially identify the remaining gaps.

Penaloza, R. (2021). A brief roadmap into uncertain knowledge representation via probabilistic description logics. ALGORITHMS, 14(10) [10.3390/a14100280].

A brief roadmap into uncertain knowledge representation via probabilistic description logics

Penaloza R.
2021

Abstract

Logic-based knowledge representation is one of the main building blocks of (logic-based) artificial intelligence. While most successful knowledge representation languages are based on classical logic, realistic intelligent applications need to handle uncertainty in an adequate manner. Over the years, many different languages for representing uncertain knowledge—often extensions of classical knowledge representation languages—have been proposed. We briefly present some of the defining properties of these languages as they pertain to the family of probabilistic description logics. This limited view is intended to help pave the way for the interested researcher to find the most adequate language for their needs, and potentially identify the remaining gaps.
No
Articolo in rivista - Articolo scientifico
Scientifica
Knowledge representation; Probabilistic reasoning; Survey; Uncertainty;
English
Penaloza, R. (2021). A brief roadmap into uncertain knowledge representation via probabilistic description logics. ALGORITHMS, 14(10) [10.3390/a14100280].
Penaloza, R
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/336363
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