Efficient Dialogue Using a Probabilistic Nested User Model

We describe a set of dialogue simulation experiments,
in which a probabilistic nested user model
is employed in deciding between speech acts for
a collaborative planning task, finding that a gain
in utility can be obtained by using a probabilistic
rather than a logical model. Given a set of ordinary
dialogue plan rules, our system generates a gametree
representation of the dialogue, using chance
nodes to represent uncertain preconditions in the
plan. Then, the game-tree is evaluated with respect
to a given user model state.