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
ARCO1: an application of belief networks to the oil market
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Reasoning with qualitative probabilities can be tractable
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Probabilistic reasoning in decision support systems: from computation to common sense
Probabilistic reasoning in decision support systems: from computation to common sense
Building Probabilistic Networks: 'Where Do the Numbers Come From?' Guest Editors' Introduction
IEEE Transactions on Knowledge and Data Engineering
A general non-probabilistic theory of inductive reasoning
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Monotonicity in Bayesian networks
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Bringing order into bayesian-network construction
Proceedings of the 3rd international conference on Knowledge capture
Bayesian network modelling through qualitative patterns
Artificial Intelligence
MUNIN: a causal probabilistic network for interpretation of electromyographic findings
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Learning Bayesian network parameters under order constraints
International Journal of Approximate Reasoning
Efficient reasoning in qualitative probabilistic networks
AAAI'93 Proceedings of the eleventh national conference on 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
Elicitation of probabilities for belief networks: combining qualitative and quantitative information
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Refining reasoning in qualitative probabilistic networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
On the relation between kappa calculus and probabilistic reasoning
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Intercausal reasoning with uninstantiated ancestor nodes
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Probabilities for a probabilistic network: a case study in oesophageal cancer
Artificial Intelligence in Medicine
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
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Qualitative probabilistic networks were designed to overcome, to at least some extent, the quantification problem known to probabilistic networks. Qualitative networks abstract from the numerical probabilities of their quantitative counterparts by using signs to summarise the probabilistic influences between their variables. One of the major drawbacks of these qualitative abstractions, however, is the coarse level of representation detail that does not provide for indicating strengths of influences. As a result, the trade-offs modelled in a network remain unresolved upon inference. We present an enhanced formalism of qualitative probabilistic networks to provide for a finer level of representation detail. An enhanced qualitative probabilistic network differs from a basic qualitative network in that it distinguishes between strong and weak influences. Now, if a strong influence is combined, upon inference, with a conflicting weak influence, the sign of the net influence may be readily determined. Enhanced qualitative networks are purely qualitative in nature, as basic qualitative networks are, yet allow for resolving some trade-offs upon inference.