Modeling Uncertainty with Propositional Assumption-Based Systems

This paper proposes assumption-based systems as an efficient and convenient way to encode uncertain information. Assumption based systems are obtained from propositional logic by including a special type of propositional symbol called assumption. Assumptions are needed to express the uncertainty of the given information. Assumption-based systems can be used to judge hypotheses qualitatively or quantitatively. This paper shows how assumption-based systems are obtained from causal networks, it describes how symbolic arguments for hypotheses can be computed efficiently, and it presents ABEL, a modeling language for assumption-based systems and an interactive tool for probabilistic assumption-based reasoning.