Conditional Plausibility Measures and Bayesian Networks
This work provides a theoretical framework for unifying various uncertainty representations in AI and reasoning, but it is incremental as it extends existing Bayesian network methods to a broader class of measures.
The paper introduces a general notion of algebraic conditional plausibility measures, showing that probability measures, ranking functions, possibility measures, and sets of probability measures can be viewed as such measures, and demonstrates that Bayesian network technology can be applied to them.
A general notion of algebraic conditional plausibility measures is defined. Probability measures, ranking functions, possibility measures, and (under the appropriate definitions) sets of probability measures can all be viewed as defining algebraic conditional plausibility measures. It is shown that the technology of Bayesian networks can be applied to algebraic conditional plausibility measures.