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Bayesian Belief Networks (BBNs) are a pow erful formalism for reasoning under uncer tainty but bear some severe limitations: they require a large amount of information be fore any reasoning process can start, they have limited contradiction handling capabil ities, and their ability to provide explana tions for their conclusion is still controversial. There exists a class of reasoning systems, called 11-uth Maintenance Systems (TMSs), which are able to deal with partially speci fied knowledge, to provide well-founded ex planation for their conclusions, and to detect and handle contradictions. TMSs incorporat ing measure of uncertainty are called Belief Maintenance Systems (BMss). This paper de scribes how a BMS based on probabilitistic logic can be applied to BBNs, thus introduc ing a new class of BBNs, called Ignorant Be lief Networks, able to incrementally deal with partially specified conditional dependencies, to provide explanations, and to detect and handle contradictions.
Belief Maintenance in Bayesian Networks