Dempster-Shafer Belief Function - A New Interpretation
This work addresses foundational issues in uncertainty reasoning for AI and decision-making, but appears incremental as it builds on existing Dempster-Shafer theory.
The paper tackles the problem of interpreting Dempster-Shafer belief functions by developing a new interpretation that ensures efficient reasoning, clear correspondences between knowledge base and real world, and alignment of reasoning processes with real-world outcomes, but does not report concrete numerical results.
We develop our interpretation of the joint belief distribution and of evidential updating that matches the following basic requirements: * there must exist an efficient method for reasoning within this framework * there must exist a clear correspondence between the contents of the knowledge base and the real world * there must be a clear correspondence between the reasoning method and some real world process * there must exist a clear correspondence between the results of the reasoning process and the results of the real world process corresponding to the reasoning process.