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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Vydáno v:Machine learning Ročník 103; číslo 3; s. 407 - 449
Hlavní autoři: Nitti, Davide, De Laet, Tinne, De Raedt, Luc
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.06.2016
Springer Nature B.V
Témata:
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
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Shrnutí: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). Furthermore, we use backward reasoning to determine which facts should be included in the partial worlds. For filtering in dynamic models we combine the static inference algorithm with particle filters and guarantee that the previous partial samples can be safely forgotten, a condition that does not hold in most logical filtering frameworks. Experiments show that the proposed framework can outperform classic sampling methods for static and dynamic inference and that it is promising for robotics and vision applications. In addition, it provides the correct results in domains in which most probabilistic programming languages fail.
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ISSN:0885-6125
1573-0565
1573-0565
DOI:10.1007/s10994-016-5558-8