Extending expressivity and flexibility of abductive logic programming

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. Pr...

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Veröffentlicht in:Journal of intelligent information systems Jg. 51; H. 3; S. 647 - 672
1. Verfasser: Ferilli, Stefano
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.12.2018
Springer Nature B.V
Schlagworte:
Artificial Intelligence
Computer Science
Data Structures and Information Theory
Information
Information Storage and Retrieval
Intelligent systems
IT in Business
Logic programming
Natural Language Processing (NLP)
Reasoning
Abductive logic programming
Probability
Constraints
ISSN:0925-9902, 1573-7675
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:0925-9902
1573-7675
DOI:10.1007/s10844-018-0531-6