Convince: a conversational inference consolidation engine
Convince: a conversational inference consolidation engine
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On the expressiveness of rule-based systems for reasoning with uncertainty
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 1
On the expressiveness of rule-based systems for reasoning with uncertainty
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Multiplicative factorization of noisy-max
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Incremental dynamic construction of layered polytree networks
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Belief updating by enumerating high-probabilityindependence-based assignments
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Global conditioning for probabilistic inference in belief networks
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Intercausal independence and heterogeneous factorization
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Causal independence for knowledge acquisition and inference
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This paper introduces a representation of evidential relationships which permits updating of belief in two simultaneous modes: causal (i. e. top-down) and diagnostic (i.e. bottom-up). It extends the hierarchical tree representation by allowing multiple causes to a given manifestation. We develop an updating scheme that obeys the axioms of probability, is computationally efficient, and is compatible with experts reasoning. The belief parameters of each variable are defined and updated by those of its neighbors in such a way that the impact of each new evidence propagates and settles through the network in a single pass.