Probabilistic inference and influence diagrams
Operations Research
Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
Reasoning with qualitative probabilities can be tractable
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
Some Varieties of Qualitative Probability
IPMU'94 Selected papers from the 5th International Conference on Processing and Management of Uncertainty in Knowledge-Based Systems, Advances in Intelligent Computing
Qualtitative propagation and scenario-based scheme for exploiting probabilistic reasoning
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Efficient reasoning in qualitative probabilistic networks
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Using qualitative relationships for bounding probability distributions
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Refining reasoning in qualitative probabilistic networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Using a Probabilistic Student Model to Control Problem Difficulty
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
Qualitative probability and order of magnitude reasoning
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Monotonicity in Bayesian networks
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Enhanced qualitative probabilistic networks for resolving trade-offs
Artificial Intelligence
Surprise-Based Qualitative Probabilistic Networks
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Qualitative probabilistic networks with reduced ambiguities
Applied Intelligence
Hierarchical qualitative inference model with substructures
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Enhancing QPNs for trade-off resolution
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
From qualitative to quantitative probabilistic networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Pivotal pruning of trade-offs in QPNs
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Using qualitative relationships for bounding probability distributions
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Qualitative probabilistic reasoning in a Bayesian network often reveals tradeoffs: relationships that are ambiguous due to competing qualitative influences. We present two techniques that combine qualitative and numeric probabilistic reasoning to resolve such tradeoffs, inferring the qualitative relationship between nodes in a Bayesian network. The first approach incrementally marginalizes nodes that contribute to the ambiguous qualitative relationships. The second approach evaluates approximate Bayesian networks for bounds of probability distributions, and uses these bounds to determinate qualitative relationships in question. This approach is also incremental in that the algorithm refines the state spaces of random variables for tighter bounds until the qualitative relationships are resolved. Both approaches provide systematic methods for tradeoff resolution at potentially lower computational cost than application of purely numeric methods.