Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
Fuzzy association degree with delayed time in temporal data model
Journal of Computer Science and Technology
Non-additive measures by interval probability functions
Information Sciences—Informatics and Computer Science: An International Journal
Introducing situational signs in qualitative probabilistic networks
International Journal of Approximate Reasoning
Efficient reasoning in qualitative probabilistic networks
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
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
Incremental tradeoff resolution in qualitative probabilistic networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Upgrading ambiguous signs in QPNs
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Hierarchical qualitative inference model with substructures
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
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A qualitative probabilistic network (QPN) is the qualitative abstraction of a Bayesian network by encoding variables and the qualitative influences between them in a directed acyclic graph. How to quantify the strengths of these influences is critical to resolve trade-offs and avoid ambiguities with conflicting signs during inference, which is hotly debated and studied in recent years. In order to provide for measuring the strengths of qualitative influences and resolving trade-offs, we take interval probability parameters as indicators of influence strengths in this paper. First, we define the conditional interval probabilities and multiplication rules that support causality representation and inference. Then we give the definition of qualitative influences associated with strengths represented by interval probabilities. Further, we propose the corresponding method for inference with the interval-probability-enhanced QPN. By the calculation of interval probabilities, the symmetry and transitivity properties are addressed. By giving a superposition method for qualitative influences associated with strengths, the composition property is interpreted. Building upon these 3 properties, the trade-offs can be well resolved.