BLOG: probabilistic models with unknown objects

Authors:
Brian Milch;Bhaskara Marthi;Stuart Russell;David Sontag;Daniel L. Ong;Andrey Kolobov
Affiliations:
Computer Science Division, University of California, Berkeley, CA;Computer Science Division, University of California, Berkeley, CA;Computer Science Division, University of California, Berkeley, CA;Computer Science Division, University of California, Berkeley, CA;Computer Science Division, University of California, Berkeley, CA;Computer Science Division, University of California, Berkeley, CA
Venue:
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Year:
2005

Citing 11
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Abstract

This paper introduces and illustrates BLOG, a formal language for defining probability models over worlds with unknown objects and identity uncertainty. BLOG unifies and extends several existing approaches. Subject to certain acyclicity constraints, every BLOG model specifies a unique probability distribution over first-order model structures that can contain varying and unbounded numbers of objects. Furthermore, complete inference algorithms exist for a large fragment of the language. We also introduce a probabilistic form of Skolemization for handling evidence.