Uncertainty in Artificial Intelligence
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Belief Maintenance in Bayesian Networks
Marco Ramoni, Alberto Riva
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.
Keywords: Belief updating and inconsistency handling, belief maintenance, algorithms for uncert
Pages: 498-505
PS Link: ftp://ego.psych.mcgill.ca/pub/ramoni/papers/aaai93.ps.Z
PDF Link: /papers/94/p498-ramoni.pdf
AUTHOR = "Marco Ramoni and Alberto Riva",
TITLE = "Belief Maintenance in Bayesian Networks",
BOOKTITLE = "Proceedings of the Tenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-94)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1994",
PAGES = "498--505"

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