Towards a Normative Theory of Scientific Evidence
This work provides a generalization of probabilistic logic to intervals and experimental observations, which is incremental in nature.
The paper tackles the problem of deriving probability intervals from objective evidence like experimental trials and propositional axioms to manage uncertainty in rule-based expert systems, with an expected application in domains such as medicine.
A scientific reasoning system makes decisions using objective evidence in the form of independent experimental trials, propositional axioms, and constraints on the probabilities of events. As a first step towards this goal, we propose a system that derives probability intervals from objective evidence in those forms. Our reasoning system can manage uncertainty about data and rules in a rule based expert system. We expect that our system will be particularly applicable to diagnosis and analysis in domains with a wealth of experimental evidence such as medicine. We discuss limitations of this solution and propose future directions for this research. This work can be considered a generalization of Nilsson's "probabilistic logic" [Nil86] to intervals and experimental observations.