Uncertainty in an unknown world

Recent advances in knowledge representation for probability models have allowed for uncertainty about the properties of objects and the relations that might hold among them. Such models, however, typically assume exact knowledge of which objects exist and of which object is which— -that is, they assume *domain closure* and *unique names*. These assumptions simplify the sample space for probability models, but are inappropriate for many real-world situations. This talk presents a formal language, BLOG, for defining probability models over worlds with unknown objects in which several terms may refer to the same object. BLOG models define generative processes that combine “factual” events that specify relationships among objects with “existence” events that generate the objects themselves. Subject to certain acyclicity constraints, every BLOG model specifies a unique probability distribution over the set of possible worlds for the first-order language. Furthermore, complete inference algorithms exist for a useful fragment of the language. I will present several example models and discuss interesting issues arising from the treatment of evidence in such languages.

Presenter(s)

Stuart Russell was born in Portsmouth, England in 1962. He received his B.A. with first-class honours in physics from Oxford University in 1982, and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is a professor of computer science, director of the Center for Intelligent Systems, and holder of the Smith-Zadeh Chair in Engineering. In 1990, he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was cowinner of the Computers and Thought Award. He was a 1996 Miller Professor of the University of California and was appointed to a Chancellor's Professorship in 2000. In 1998, he gave the Forsythe Memorial Lectures at Stanford University. He is a Fellow and former Executive Council member of the American Association for Artificial Intelligence and a Fellow of the Association for Computing Machinery. He has published over 100 papers on a wide range of topics in artificial intelligence. His books include "The Use of Knowledge in Analogy and Induction" (Pitman, 1989), "Do the Right Thing: Studies in Limited Rationality" (with Eric Wefald, MIT Press, 1991), and "Artificial Intelligence: A Modern Approach" (with Peter Norvig, Prentice Hall, 1995, 2003).