DagSemProc.05051.4.pdf
- Filesize: 113 kB
- 6 pages
Document
BLOG: Probabilistic Models with Unknown Objects
Authors
-
Part of:
Volume:
Dagstuhl Seminar Proceedings, Volume 5051
Series: Dagstuhl Seminar Proceedings (DagSemProc) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2006-01-19
Cite As Get BibTex
Brian Milch, Bhaskara Marthi, Stuart Russell, David Sontag, Daniel L. Ong, and Andrey Kolobov. BLOG: Probabilistic Models with Unknown Objects. In Probabilistic, Logical and Relational Learning - Towards a Synthesis. Dagstuhl Seminar Proceedings, Volume 5051, pp. 1-6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)
https://doi.org/10.4230/DagSemProc.05051.4
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.
Subject Classification
Keywords
- Knowledge representation
- probability
- first-order logic
- identity uncertainty
- unknown objects
Metrics
- Access Statistics
-
Total Accesses (updated on a weekly basis)
0PDF Downloads