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.
@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/entities/document/10.4230/DagSemProc.05051.4}, 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} }
Feedback for Dagstuhl Publishing