BLOG: Probabilistic Models with Unknown Objects

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.

Authors

• Brian Milch Luke S. Zettlemoyer
• Bhaskara Marthi
• Stuart Russell Center for Human-Compatible AI, UC Berkeley
• David Sontag
• Daniel L. Ong
• Andrey

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