Normalization and the Representation of Nonmonotonic Knowledge in the Theory of Evidence
This addresses the problem of representing uncertain and commonsense knowledge in AI systems, but appears incremental as it builds on existing Dempster-Shafer theory.
The paper tackles the representation of nonmonotonic knowledge in the Dempster-Shafer theory of evidence by introducing a monotonicity concept related to belief and plausibility ranges, showing that knowledge accumulation in this framework is nonmonotonic and can represent commonsense knowledge.
We discuss the Dempster-Shafer theory of evidence. We introduce a concept of monotonicity which is related to the diminution of the range between belief and plausibility. We show that the accumulation of knowledge in this framework exhibits a nonmonotonic property. We show how the belief structure can be used to represent typical or commonsense knowledge.