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.
Original language | English (US) |
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Pages (from-to) | 1352-1359 |
Number of pages | 8 |
Journal | IJCAI International Joint Conference on Artificial Intelligence |
State | Published - 2005 |
Event | 19th International Joint Conference on Artificial Intelligence, IJCAI 2005 - Edinburgh, United Kingdom Duration: Jul 30 2005 → Aug 5 2005 |
ASJC Scopus subject areas
- Artificial Intelligence