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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Published in:	Machine learning Vol. 103; no. 3; pp. 407 - 449
Main Authors:	Nitti, Davide, De Laet, Tinne, De Raedt, Luc
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.06.2016 Springer Nature B.V
Subjects:	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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Abstract	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.
AbstractList	Issue Title: Special Issue on Inductive Logic Programming; Guest Editors: Jesse Davis and Jan Ramon 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. 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.
Author	De Raedt, Luc Nitti, Davide De Laet, Tinne
Author_xml	– sequence: 1 givenname: Davide orcidid: 0000-0002-0031-6094 surname: Nitti fullname: Nitti, Davide email: davide.nitti@cs.kuleuven.be organization: Department of Computer Science, KU Leuven – sequence: 2 givenname: Tinne surname: De Laet fullname: De Laet, Tinne organization: Faculty of Engineering Science, KU Leuven – sequence: 3 givenname: Luc surname: De Raedt fullname: De Raedt, Luc organization: Department of Computer Science, KU Leuven
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Cites_doi	10.1093/biomet/83.1.81 10.1201/b11322 10.1080/10618600.1996.10474692 10.1115/1.3662552 10.1145/16856.16859 10.1109/IROS.2009.5354602 10.1023/A:1008935410038 10.1007/978-3-540-85928-4_17 10.7551/mitpress/7432.001.0001 10.1214/10-STS325 10.1016/0743-1066(91)90027-M 10.1080/01621459.1999.10474153 10.3182/20090706-3-FR-2004.00129 10.1214/10-BA525 10.1007/s10994-010-5213-8 10.1007/978-3-540-78246-9_32 10.1109/ICRA.2014.6906966 10.1109/IROS.2013.6696747 10.1016/j.inffus.2008.05.005 10.1007/978-3-540-78652-8 10.1109/78.978383 10.1007/s10852-012-9213-5 10.1007/978-3-540-89982-2_22 10.1109/JPROC.2012.2200552 10.1007/978-3-662-44845-8_45 10.1017/S1471068411000238 10.1109/CDC.2005.1582177 10.1007/978-1-4757-3437-9_20 10.1007/978-1-4757-4145-2 10.1007/978-3-642-83189-8 10.1016/j.artint.2010.04.015 10.1111/1467-9868.00280 10.1613/jair.1675
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Snippet	We introduce a probabilistic language and an efficient inference algorithm based on distributional clauses for static and dynamic inference in hybrid... Issue Title: Special Issue on Inductive Logic Programming; Guest Editors: Jesse Davis and Jan Ramon We introduce a probabilistic language and an efficient...
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SubjectTerms	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
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