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• W1726322587 abstract "We propose a new formal language for the expressive representation of probabilistic knowledge based on Answer Set Programming (ASP). It allows for the annotation of first-order formulas as well as ASP rules and facts with probabilities and for learning of such weights from data (parameter estimation). Weighted formulas are given a semantics in terms of soft and hard constraints which determine a probability distribution over answer sets. In contrast to related approaches, we approach inference by optionally utilizing so-called streamlining XOR constraints, in order to reduce the number of computed answer sets. Our approach is prototypically implemented. Examples illustrate the introduced concepts and point at issues and topics for future research." @default.
• W1726322587 created "2016-06-24" @default.
• W1726322587 creator A5016411514 @default.
• W1726322587 creator A5077853141 @default.
• W1726322587 date "2014-05-04" @default.
• W1726322587 modified "2023-09-27" @default.
• W1726322587 title "Probabilistic Inductive Logic Programming Based on Answer Set Programming" @default.
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• W1726322587 hasPublicationYear "2014" @default.
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