Probabilistic logic programming for hybrid relational domains

We introduce a probabilistic language and an efficient inference algorithm based on distributional clauses for static and dynamic inference in hybrid relational domains. Static inference is based on sampling, where the samples represent (partial) worlds (with discrete and continuous variables). Furt...

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Veröffentlicht in:	Machine learning Jg. 103; H. 3; S. 407 - 449
Hauptverfasser:	Nitti, Davide, De Laet, Tinne, De Raedt, Luc
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.06.2016 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Computer programming Computer Science Control Discrete andcontinuous distributions Dynamics Filtering Filtration Inference Learning Likelihood weighting Logic programming Mechatronics Natural Language Processing (NLP) Particle filter Probabilistic methods Probabilistic programming Probability theory Robotics Simulation and Modeling Statistical relational learning Statistics Likelihood weighting Statistical relational learning Logic programming Probabilistic programming Discrete and continuous distributions Particle filter
ISSN:	0885-6125, 1573-0565, 1573-0565
Online-Zugang:	Volltext
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