Typical models: minimizing false beliefs
  A knowledge system S describing a part of real world does in general not
contain complete information. Reasoning with incomplete information is prone to
errors since any belief derived from S may be false in the present state of the
world. A false belief may suggest wrong decisions and lead to harmful actions.
So an important goal is to make false beliefs as unlikely as possible. This
work introduces the notions of "typical atoms" and "typical models", and shows
that reasoning with typical models minimizes the expected number of false
beliefs over all ways of using incomplete information. Various properties of
typical models are studied, in particular, correctness and stability of beliefs
suggested by typical models, and their connection to oblivious reasoning.
