Measures of truth and probabilistic graphical models for representation of knowledge with uncertainty

Abstract

For representation of knowledge with uncertainty both mathematical formalism allowing to describe and handle uncertainty and theoretical computer model limiting memory and time used for such representation and its processing, are required. The paper gives an overview of the main truth measures including probability measure that being applied in articial intelligence for uncertainty representation, and probabilistic graphical models which allow to limit the growth of processing algorithms complicity and memory requirements for modelling knowledge with uncertainty by means of computation localisation.