Inverse resolution as belief change
Maurice Pagnucco, David Rajaratnam · 2005
Belief change is concerned with modelling the way in which a rational reasoner maintains its beliefs as it acquires new information. Of particular interest is the way in which new beliefs are acquired and determined and old beliefs are retained or discarded. A parallel can be drawn to symbolic machine learning approaches where examples to be categorised are presented to the learning system and a theory is subsequently derived, usually over a number of iterations. It is therefore not surprising that the term ‘theory revision ’ is used to describe this process [Ourston and Mooney, 1994]. Viewing a machine learning system as a rational reasoner allows us to begin seeing these seemingly disparate mechanisms