First-order probabilistic inference
Published 9 August 2003
David Poole
Citations420
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TL;DR
This paper presents an algorithm to reason about multiple individuals, where the authors may know particular facts about some of them, but want to treat the others as a group.
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.
Keywords
Computer Science
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