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- Title
Extending expressivity and flexibility of abductive logic programming.
- Authors
Ferilli, Stefano
- 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.
- Subjects
LOGIC programming; INCOMPLETENESS theorems; PROBABILITY theory; INFORMATION retrieval; DATA analysis
- Publication
Journal of Intelligent Information Systems, 2018, Vol 51, Issue 3, p647
- ISSN
0925-9902
- Publication type
Article
- DOI
10.1007/s10844-018-0531-6