Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Formulation of tradeoffs in planning under uncertainty
Formulation of tradeoffs in planning under uncertainty
A symbolic approach to reasoning with linguistic quantifiers
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Monotonic influence diagrams: application to optimal and robust design
Monotonic influence diagrams: application to optimal and robust design
Arguments, contradictions and practical reasoning
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Qualitative probabilities: a normative framework for commonsense reasoning
Qualitative probabilities: a normative framework for commonsense reasoning
Qualtitative propagation and scenario-based scheme for exploiting probabilistic reasoning
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Numeric reasoning with relative orders of magnitude
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Efficient reasoning in qualitative probabilistic networks
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Context-specific sign-propagation in qualitative probabilistic networks
Artificial Intelligence
Qualitative probability and order of magnitude reasoning
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Towards qualitative approaches to Bayesian evidential reasoning
Proceedings of the 11th international conference on Artificial intelligence and law
Enhanced qualitative probabilistic networks for resolving trade-offs
Artificial Intelligence
Inference in qualitative probabilistic networks revisited
International Journal of Approximate Reasoning
Conceptions of Vagueness in Subjective Probability for Evidential Reasoning
Proceedings of the 2009 conference on Legal Knowledge and Information Systems: JURIX 2009: The Twenty-Second Annual Conference
Compositional Bayesian modelling for computation of evidence collection strategies
Applied Intelligence
Enhancing QPNs for trade-off resolution
UAI'99 Proceedings of the Fifteenth 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
Incremental tradeoff resolution in qualitative probabilistic networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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In recent years there has been a spate of papers describing systems for probabilisitic reasoning which do not use numerical probabilities. In some cases the simple set of values used by these systems make it impossible to predict how a probability will change or which hypothesis is most likely given certain evidence. This paper concentrates on such situations, and suggests a number of ways in which they may be resolved by refining the representation.