An Interpretation for the Conditional Belief Function in The Theory of Evidence

. The paper provides a frequency-based interpretation for conditional belief functions that overcomes the well-formedness problem of DST belief networks by identifying a class of conditional belief functions for which well-formedness is granted. Key words: Knowledge Representation and Integration, Soft Computing, evidence theory, graphoidal structures, conditional belief functions, well-formedness 1 Introduction It is commonly acknowledged that we need to accept and handle uncertainty when reasoning with real world data, including vagueness and incompleteness of knowledge. The Mathematical Theory of Evidence or the Dempster-Shafer Theory (DST) [2, 14] has been intensely investigated in the past as a means of expressing incomplete knowledge. A number of implementations in various fields apparently confirm the usefulness of this model of representation and processing of uncertainty (e.g. reliability in real-time X-ray radioscopy and ultrasounds [3], multisensor image segmentation [1], ..