Uncertainty in Artificial Intelligence
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Maintenance in Probabilistic Knowledge-Based Systems
Thomas Reid, Gregory Parnell
Abstract:
Recent developments using directed acyclical graphs (i.e., influence diagrams and Bayesian networks) for knowledge representation have lessened the problems of using probability in knowledge-based systems (KBS). Most current research involves the efficient propagation of new evidence, but little has been done concerning the maintenance of domain-specific knowledge, which includes the probabilistic information about the problem domain. By making use of conditional independencies represented in she graphs, however, probability assessments are required only for certain variables when the knowledge base is updated. The purpose of this study was to investigate, for those variables which require probability assessments, ways to reduce the amount of new knowledge required from the expert when updating probabilistic information in a probabilistic knowledge-based system. Three special cases (ignored outcome, split outcome, and assumed constraint outcome) were identified under which many of the original probabilities (those already in the knowledge-base) do not need to be reassessed when maintenance is required.
Keywords:
Pages: 291-298
PS Link:
PDF Link: /papers/88/p291-reid.pdf
BibTex:
@INPROCEEDINGS{Reid88,
AUTHOR = "Thomas Reid and Gregory Parnell",
TITLE = "Maintenance in Probabilistic Knowledge-Based Systems",
BOOKTITLE = "Proceedings of the Fourth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-88)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "1988",
PAGES = "291--298"
}


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