License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagSemProc.05051.4
URN: urn:nbn:de:0030-drops-4169
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2006/416/
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Milch, Brian ; Marthi, Bhaskara ; Russell, Stuart ; Sontag, David ; Ong, Daniel L. ; Kolobov, Andrey

BLOG: Probabilistic Models with Unknown Objects

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Abstract

We introduce BLOG, a formal language for defining probability models with unknown objects and identity uncertainty. A BLOG model describes a generative process in which some steps add objects to the world, and others determine attributes and relations on these objects. Subject to certain acyclicity constraints, a BLOG model specifies a unique probability distribution over first-order model structures that can contain varying and unbounded numbers of objects. Furthermore, inference algorithms exist for a large class of BLOG models.

BibTeX - Entry

@InProceedings{milch_et_al:DagSemProc.05051.4,
  author =	{Milch, Brian and Marthi, Bhaskara and Russell, Stuart and Sontag, David and Ong, Daniel L. and Kolobov, Andrey},
  title =	{{BLOG: Probabilistic Models with Unknown Objects}},
  booktitle =	{Probabilistic, Logical and Relational Learning - Towards a Synthesis},
  pages =	{1--6},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5051},
  editor =	{Luc De Raedt and Thomas Dietterich and Lise Getoor and Stephen H. Muggleton},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2006/416},
  URN =		{urn:nbn:de:0030-drops-4169},
  doi =		{10.4230/DagSemProc.05051.4},
  annote =	{Keywords: Knowledge representation, probability, first-order logic, identity uncertainty, unknown objects}
}

Keywords: Knowledge representation, probability, first-order logic, identity uncertainty, unknown objects
Collection: 05051 - Probabilistic, Logical and Relational Learning - Towards a Synthesis
Issue Date: 2006
Date of publication: 19.01.2006

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