Probabilistic Assumption-Based Reasoning

TL;DR

This paper extends assumption-based reasoning to include probabilities, integrating evidence theory and providing mathematical foundations and computational methods for support degree calculation.

Contribution

It develops a rigorous mathematical framework for probabilistic assumption-based reasoning and adapts existing computational techniques for support degree evaluation.

Findings

Mathematical foundations for probabilistic assumption-based reasoning

Integration of evidence theory into assumption-based models

Adaptation of computational methods for support degree calculation

Abstract

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.