Probabilistic Assumption-Based Reasoning
This work provides a foundational mathematical treatment for probabilistic assumption-based reasoning, which is incremental relative to prior proposals.
The paper extends classical propositional assumption-based reasoning by incorporating probabilities for assumptions and placing it within the evidence theory framework, thoroughly developing its mathematical foundations and computational methods.
The classical propositional assumption-based model is extended to incorporate probabilities for the assumptions. Then it is placed into the framework of evidence theory. Several authors like Laskey, Lehner (1989) and Provan (1990) already proposed a similar point of view, but the first paper is not as much concerned with mathematical foundations, and Provan's paper develops into a different direction. Here we thoroughly develop and present the mathematical foundations of this theory, together with computational methods adapted from Reiter, De Kleer (1987) and Inoue (1992). Finally, recently proposed techniques for computing degrees of support are presented.