Typical models: minimising false beliefs
Eliezer L. Lozinskii · Journal of Experimental & Theoretical Artificial Intelligence · 2010
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 minimises 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.