  A framework is presented for a computational theory of probabilistic
argument. The Probabilistic Reasoning Environment encodes knowledge at three
levels. At the deepest level are a set of schemata encoding the system's domain
knowledge. This knowledge is used to build a set of second-level arguments,
which are structured for efficient recapture of the knowledge used to construct
them. Finally, at the top level is a Bayesian network constructed from the
arguments. The system is designed to facilitate not just propagation of beliefs
and assimilation of evidence, but also the dynamic process of constructing a
belief network, evaluating its adequacy, and revising it when necessary.
