Belief Revision with Uncertain Inputs in the Possibilistic Setting
It addresses incremental theoretical improvements in belief revision for AI and logic systems, focusing on uncertain inputs in a possibilistic setting.
This paper tackles belief revision with uncertain inputs using possibility theory, exploring two conditioning operations (min-based and product-based) and two interpretations of inputs, and shows that it can capture existing revision methods like Williams' transmutations and Boutilier's natural revision.
This paper discusses belief revision under uncertain inputs in the framework of possibility theory. Revision can be based on two possible definitions of the conditioning operation, one based on min operator which requires a purely ordinal scale only, and another based on product, for which a richer structure is needed, and which is a particular case of Dempster's rule of conditioning. Besides, revision under uncertain inputs can be understood in two different ways depending on whether the input is viewed, or not, as a constraint to enforce. Moreover, it is shown that M.A. Williams' transmutations, originally defined in the setting of Spohn's functions, can be captured in this framework, as well as Boutilier's natural revision.