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
Probabilistic reasoning in decision support systems: from computation to common sense
Probabilistic reasoning in decision support systems: from computation to common sense
Mathematics and Computers in Simulation - Special issue: 3rd IMACS international workshop on qualitative reasoning and decision support systems
Strategic decision-making processes: network-based representation and stochastic simulation
Decision Support Systems - Special issue: expertise and modeling expert decision making
Context-specific sign-propagation in qualitative probabilistic networks
Artificial Intelligence
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
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
A framework for linking advanced simulation models with interactive cognitive maps
Environmental Modelling & Software
ER '07 Tutorials, posters, panels and industrial contributions at the 26th international conference on Conceptual modeling - Volume 83
Using Qualitative Probability in Reverse-Engineering Gene Regulatory Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Hierarchical qualitative inference model with substructures
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Qualitative chain graphs and their application
International Journal of Approximate Reasoning
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Qualitative probabilistic networks (QPNs) are basically qualitative derivations of Bayesian belief networks. Originally, QPNs were designed to improve the speed of the construction and calculation of these networks, at the cost of specificity of the result. The formalism can also be used to facilitate cognitive mapping by means of inference in sign-based causal diagrams. Whatever the type of application, any computer based use of QPNs requires an algorithm capable of propagating information throughout the networks. Such an algorithm was developed in the 1990s. This polynomial time sign-propagation algorithm is explicitly or implicitly used in most existing QPN studies. This paper firstly shows that two types of undesired results may occur with the original sign-propagation algorithm: the results can be (1) less specific than possible at the given level of abstraction, or, more seriously (2) incorrect. Secondly, the paper identifies the causes underlying these problems. Thirdly, this paper presents an adapted sign-propagation algorithm. The worst-case running time of the adapted algorithm is still polynomial in the number of arrows. The results of the new algorithm have been compared with those of the original algorithm by applying both algorithms to a real-life constructed cognitive map. It is shown that the problems of the original algorithm are indeed prevented with the adapted algorithm.