Any artificial intelligence tool designed for cyber-attribution must deal with information coming from different sources that invariably leads to incompleteness, overspecification, or inherently uncertain content. The presence of these varying levels of uncertainty doesn’t mean that the information is worthless—rather, these are hurdles that the knowledge engineer must learn to work with. In this chapter, we continue developing the DeLP3E model introduced in the previous chapter, focusing now on the problem of belief revision in DeLP3E. We first propose a non-prioritized class of revision operators called AFO (Annotation Function-based Operators); then, we go on to argue that in some cases it may be desirable to define revision operators that take quantitative aspects into account (such as how the probabilities of certain literals or formulas of interest change after the revision takes place). As a result, we propose the QAFO (Quantitative Annotation Function-based Operators) class of operators, a subclass of AFO, and study the complexity of several problems related to their specification and application in revising knowledge bases. Finally, we present an algorithm for computing the probability that a literal is warranted in a DeLP3E knowledge base, and discuss how it could be applied towards implementing QAFO-style operators that compute approximations rather than exact operations.