On the negation of a Dempster-Shafer belief structure based on maximum uncertainty allocation
This addresses a theoretical gap in uncertainty representation for researchers and practitioners in fields like AI and decision-making, but appears incremental as it extends prior work on probability distributions.
The paper tackled the problem of defining the negation of a belief structure in Dempster-Shafer theory, which was an open issue, by proposing a transformation based on maximum uncertainty allocation and showing it is compatible with existing probability distribution methods.
Probability theory and Dempster-Shafer theory are two germane theories to represent and handle uncertain information. Recent study suggested a transformation to obtain the negation of a probability distribution based on the maximum entropy. Correspondingly, determining the negation of a belief structure, however, is still an open issue in Dempster-Shafer theory, which is very important in theoretical research and practical applications. In this paper, a negation transformation for belief structures is proposed based on maximum uncertainty allocation, and several important properties satisfied by the transformation have been studied. The proposed negation transformation is more general and could totally compatible with existing transformation for probability distributions.