A MATHEMATICAL-MODEL OF UNCERTAIN-INFORMATION

This paper introduces a mathematical evidence model for uncertain information in artificial intelligence. Each evidence model contains prior information as well as possible new evidence to appear later. Both Bayesian probability distribution and Dempster-Shafer's ignorance are special evidence models. A concept of independence is also introduced. Dempster-Shafer's combination rule becomes a formula to combine basic probabilities of independent models.