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ECAI
2008
Springer
15 years 1 months ago
Structure Learning of Markov Logic Networks through Iterated Local Search
Many real-world applications of AI require both probability and first-order logic to deal with uncertainty and structural complexity. Logical AI has focused mainly on handling com...
Marenglen Biba, Stefano Ferilli, Floriana Esposito
IBERAMIA
2004
Springer
15 years 5 months ago
Decomposing Ordinal Sums in Neural Multi-adjoint Logic Programs
The theory of multi-adjoint logic programs has been introduced as a unifying framework to deal with uncertainty, imprecise data or incomplete information. From the applicative part...
Jesús Medina, Enrique Mérida Caserme...
JASIS
2000
120views more  JASIS 2000»
14 years 11 months ago
Probabilistic datalog: Implementing logical information retrieval for advanced applications
In the logical approach to information retrieval (IR), retrieval is considered as uncertain inference. Whereas classical IR models are based on propositional logic, we combine Dat...
Norbert Fuhr
LPAR
2010
Springer
14 years 9 months ago
Semiring-Induced Propositional Logic: Definition and Basic Algorithms
In this paper we introduce an extension of propositional logic that allows clauses to be weighted with values from a generic semiring. The main interest of this extension is that ...
Javier Larrosa, Albert Oliveras, Enric Rodrí...
ICML
2007
IEEE
16 years 19 days ago
Bottom-up learning of Markov logic network structure
Markov logic networks (MLNs) are a statistical relational model that consists of weighted firstorder clauses and generalizes first-order logic and Markov networks. The current sta...
Lilyana Mihalkova, Raymond J. Mooney