Belief maintenance in Bayesian networks

Authors:
Marco Ramoni;Alberto Riva
Affiliations:
Cognitive Studies in Medicine, McGill Cognitive Science Centre, McGill University, Montreal, Canada;Laboratorio di Informatica Medica, Dipartimento di Informatica e Sistemistica, Università di Pavia, Pavia, Italy
Venue:
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Year:
1994

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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 probabilitistic 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.