Modelling uncertainty with propositional assumption-based systems

This paper proposes assumption-based systems as an efficient and convenient way to encode uncertain information. Assumptionbased 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. Assumptionbased 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.