Hybrid Possibilistic Conditioning for Revision under Weighted Inputs
Salem Benferhat, Célia da Costa Pereira, Andrea G. B. Tettamanzi · Frontiers in artificial intelligence and applications · 2012
We propose and investigate new operators in the possibilistic belief revision setting, obtained as different combinations of the conditioning operators on models and countermodels, as well as of how weighted inputs are interpreted. We obtain a family of eight operators that essentially obey the basic postulates of revision, with a few slight differences. These operators show an interesting variety of behaviors, making them suitable to representing changes in the beliefs of an agent in different contexts.