A Framework for Non-Monotonic Reasoning About Probabilistic Assumptions

TL;DR

This paper introduces a framework for non-monotonic reasoning in probabilistic expert systems, addressing the need for revisable assumptions and interdependency judgments in probabilistic analysis.

Contribution

It proposes a novel framework that allows expert systems to handle non-monotonic probabilistic reasoning, enabling iterative model validation and assumption revision.

Findings

Framework supports revisable probabilistic assumptions

Enhances expert systems with non-monotonic reasoning capabilities

Addresses limitations of modular rule-based probabilistic models

Abstract

Attempts to replicate probabilistic reasoning in expert systems have typically overlooked a critical ingredient of that process. Probabilistic analysis typically requires extensive judgments regarding interdependencies among hypotheses and data, and regarding the appropriateness of various alternative models. The application of such models is often an iterative process, in which the plausibility of the results confirms or disconfirms the validity of assumptions made in building the model. In current expert systems, by contrast, probabilistic information is encapsulated within modular rules (involving, for example, "certainty factors"), and there is no mechanism for reviewing the overall form of the probability argument or the validity of the judgments entering into it.