Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
On the Role of Context-Specific Independence in Probabilistic Inference
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Enhancing QPNs for trade-off resolution
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Elicitation of probabilities for belief networks: combining qualitative and quantitative information
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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Qualitative probabilistic networks represent probabilistic influences between variables. Due to the level of representation detail provided, knowledge about influences that hold only in specific contexts cannot be expressed. The results computed from a qualitative network, as a consequence, can be quite weak and uninformative. We extend the basic formalism of qualitative probabilistic networks by providing for the inclusion of context-specific information about influences and show that exploiting this information upon inference has the ability to forestall unnecessarily weak results.