Charniak, E.

A Bayesian model of plan recognition


We argue that the problem of plan recognition, inferring an agent's plan from observations, is largely a problem of inference under conditions of uncertainty. We present an approach to the plan recognition problem that is based on Bayesian probability theory. In attempting to solve a plan recognition problem we first retrieve candidate explanations. These explanations (sometimes only the most promising ones) are assembled into a plan recognition Bayesian network, which is a representation of a probability distribution over the set of possible explanations.

Inference and knowledge in language comprehension.


For example, structural disambiguation: (1) Waiter, I would like spaghetti with meat sauce and wine.);

Toward a model of children's story comprehension


This report considers the problem of constructing an abstract model of story comprehension. The use of questions that go beyond the story as a test of understanding the story raises a methodological problem which is discussed in detail.