First-order probabilistic inference

Authors:
David Poole
Affiliations:
Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
Venue:
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Year:
2003

Citing 12
Cited 62

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Abstract

There have been many proposals for first-order belief networks (i.e., where we quantify over individuals) but these typically only let us reason about the individuals that we know about. There are many instances where we have to quantify over all of the individuals in a population. When we do this the population size often matters and we need to reason about all of the members of the population (but not necessarily individually). This paper presents an algorithm to reason about multiple individuals, where we may know particular facts about some of them, but want to treat the others as a group. Combining unification with variable elimination lets us reason about classes of individuals without needing to ground out the theory.