  Plan recognition does not work the same way in stories and in "real life"
(people tend to jump to conclusions more in stories). We present a theory of
this, for the particular case of how objects in stories (or in life) influence
plan recognition decisions. We provide a Bayesian network formalization of a
simple first-order theory of plans, and show how a particular network parameter
seems to govern the difference between "life-like" and "story-like" response.
We then show why this parameter would be influenced (in the desired way) by a
model of speaker (or author) topic selection which assumes that facts in
stories are typically "relevant".
