in Proceedings IJCAI 2003
., Acapulco, Mexico,
August 2003, pages 985-991.
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.
A revised version of the paper is available in pdf
format. This corrects some mixing of the words, "variable" and
"paramter", fixes Figure 3 and example 7.
You can also get the slides from the talk.