Belief Maintenance in Bayesian Networks

TL;DR

This paper introduces Ignorant Belief Networks, a new class of Bayesian networks that incorporate belief maintenance systems to handle partial information, provide explanations, and manage contradictions more effectively.

Contribution

It presents a novel integration of belief maintenance systems with Bayesian networks, enabling incremental reasoning with partial knowledge and improved explanation capabilities.

Findings

Able to handle partially specified conditional dependencies

Provides explanations for reasoning processes

Detects and manages contradictions effectively

Abstract

Bayesian Belief Networks (BBNs) are a powerful formalism for reasoning under uncertainty but bear some severe limitations: they require a large amount of information before any reasoning process can start, they have limited contradiction handling capabilities, and their ability to provide explanations for their conclusion is still controversial. There exists a class of reasoning systems, called Truth Maintenance Systems (TMSs), which are able to deal with partially specified knowledge, to provide well-founded explanation for their conclusions, and to detect and handle contradictions. TMSs incorporating measure of uncertainty are called Belief Maintenance Systems (BMSs). This paper describes how a BMS based on probabilistic logic can be applied to BBNs, thus introducing a new class of BBNs, called Ignorant Belief Networks, able to incrementally deal with partially specified conditional dependencies, to provide explanations, and to detect and handle contradictions.