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ICML
2005
IEEE
16 years 7 months ago
Learning the structure of Markov logic networks
Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. In this pap...
Stanley Kok, Pedro Domingos
JSAI
2005
Springer
16 years 10 days ago
Learning Stochastic Logical Automaton
Abstract. This paper is concerned with algorithms for the logical generalisation of probabilistic temporal models from examples. The algorithms combine logic and probabilistic mode...
Hiroaki Watanabe, Stephen Muggleton
190
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DALT
2004
Springer
16 years 7 days ago
The Logic of Communication Graphs
In 1992, Moss and Parikh studied a bimodal logic of knowledge and effort called Topologic. In this current paper, Topologic is extended to the case of many agents who are assumed...
Eric Pacuit, Rohit Parikh
JELIA
2004
Springer
16 years 6 days ago
Logic Programs with Functions and Default Values
In this work we reconsider the replacement of predicate-like notation by functional terms, using a similar syntax to Functional Logic Programming, but under a completely different...
Pedro Cabalar, David Lorenzo
SPLC
2007
15 years 8 months ago
Feature Diagrams and Logics: There and Back Again
Feature modeling is a notation and an approach for modeling commonality and variability in product families. In their basic form, feature models contain mandatory/optional feature...
Krzysztof Czarnecki, Andrzej Wasowski