Applications of circumscription to formalizing common-sense knowledge
Artificial Intelligence
Readings in nonmonotonic reasoning
Readings in nonmonotonic reasoning
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
Search-based methods to bound diagnostic probabilities in very large belief nets
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
A symbolic generalization of probability theory
A symbolic generalization of probability theory
Probabilistic reasoning in decision support systems: from computation to common sense
Probabilistic reasoning in decision support systems: from computation to common sense
Default Reasoning: Causal and Conditional Theories
Default Reasoning: Causal and Conditional Theories
A Maximum Entropy Approach to Nonmonotonic Reasoning
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Introduction to Algorithms for Inference in Belief Nets
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
System-Z+: a formalism for reasoning with variable-strength defaults
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Efficient reasoning in qualitative probabilistic networks
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
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Qualitative and infinitesimal probability schemes are consistent with the axioms of probability theory, but avoid the need for precise numerical probabilities. Using qualitative probabilities could substantially reduce the effort for knowledge engineering and improve the robustness of results. We examine experimentally how well infinitesimal probabilities (the kappa-calculus of Goldszmidt and Pearl) perform a diagnostic task -- troubleshooting a car that will not start -- by comparison with a conventional numerical belief network. We found the infinitesimal scheme to be as good as the numerical scheme in identifying the true fault. The performance of the infinitesimal scheme worsens significantly for prior fault probabilities greater than 0.03. These results suggest that infinitesimal probability methods may be of substantial practical value for machine diagnosis with small prior fault probabilities.