Dempster-Shafer vs. Probabilistic Logic
This work addresses foundational issues in uncertainty reasoning for AI and decision-making, but it is incremental as it builds on existing theories without introducing new methods.
The paper compared evidence combination in Dempster-Shafer theory and probabilistic logic, identifying sufficient conditions for agreement and showing that disagreement can occur when these conditions are not met, with an example demonstrating radically different results under specific assumptions.
The combination of evidence in Dempster-Shafer theory is compared with the combination of evidence in probabilistic logic. Sufficient conditions are stated for these two methods to agree. It is then shown that these conditions are minimal in the sense that disagreement can occur when any one of them is removed. An example is given in which the traditional assumption of conditional independence of evidence on hypotheses holds and a uniform prior is assumed, but probabilistic logic and Dempster's rule give radically different results for the combination of two evidence events.