Abnormality Counts ! MSc

Defeasible inheritance networks provide a fruitful environment for modeling default reasoning. In this paper we aim to enhance their expressive power with the idea of abnormality minimisation, which plays a crucial role in circumscription. We implement it in two alternative network-based frameworks. The rst is in line with the received way of modeling default reasoning by means of inheritance nets, that is to say, it is path-based or direct. The second relies on a set of non-monotonic inference rules. In both we are able to say that an object is abnormal with respect to a certain default statement, and, consequently, single out the conclusion sets that imply the least number of abnormalities. We consider some cases indicating that exactly these sets are the intuitively correct ones.