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- W2896772039 abstract "Real-world problems often require purely deductive reasoning to be supported by other techniques that can cope with noise in the form of incomplete and uncertain data. Abductive inference tackles incompleteness by guessing unknown information, provided that it is compliant with given constraints. Probabilistic reasoning tackles uncertainty by weakening the sharp logical approach. This work aims at bringing both together and at further extending the expressive power of the resulting framework, called Probabilistic Expressive Abductive Logic Programming (PEALP). It adopts a Logic Programming perspective, introducing several kinds of constraints and allowing to set a degree of strength on their validity. Procedures to handle both extensions, compatibly with standard abductive and probabilistic frameworks, are also provided." @default.
- W2896772039 created "2018-10-26" @default.
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- W2896772039 date "2018-10-09" @default.
- W2896772039 modified "2023-09-23" @default.
- W2896772039 title "Extending expressivity and flexibility of abductive logic programming" @default.
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- W2896772039 doi "https://doi.org/10.1007/s10844-018-0531-6" @default.
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