A Probabilistic Reasoning Environment

TL;DR

This paper introduces a probabilistic reasoning framework that constructs and revises belief networks dynamically using layered knowledge representations, enhancing belief propagation and evidence assimilation.

Contribution

It presents a novel multi-level probabilistic reasoning environment that integrates domain knowledge, argument structures, and Bayesian networks for dynamic belief management.

Findings

Effective dynamic construction of belief networks

Improved belief propagation and evidence integration

Framework supports belief revision and evaluation

Abstract

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.