Propositional knowledge base revision and minimal change
Artificial Intelligence
Social choice axioms for fuzzy set aggregation
Fuzzy Sets and Systems - Special issue: Aggregation and best choices of imprecise opinions
Reasoning with qualitative probabilities can be tractable
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
On the logic of iterated belief revision
Artificial Intelligence
Belief revision with unreliable observations
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A Thorough Axiomatization of a Principle of Conditional Preservation in Belief Revision
Annals of Mathematics and Artificial Intelligence
System JLZ: rational default reasoning by minimal ranking constructions
Journal of Applied Logic - Special issue on combining probability and logic
Iterated belief revision, revised
Artificial Intelligence
Admissible and restrained revision
Journal of Artificial Intelligence Research
Representation theorems for multiple belief changes
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Change rules for hierarchical beliefs
International Journal of Approximate Reasoning
A general framework for measuring inconsistency through minimal inconsistent sets
Knowledge and Information Systems
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Projective default epistemology
WCII'02 Proceedings of the 2002 international conference on Conditionals, Information, and Inference
Revision over partial pre-orders: a postulational study
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
A characteristic function approach to inconsistency measures for knowledge bases
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Evidential fusion for gender profiling
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
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The success postulate in belief revision ensures that new evidence (input) is always trusted. However, admitting uncertain input has been questioned by many researchers. Darwiche and Pearl argued that strengths of evidence should be introduced to determine the outcome of belief change, and provided a preliminary definition towards this thought. In this paper, we start with Darwiche and Pearl's idea aiming to develop a framework that can capture the influence of the strengths of inputs with some rational assumptions. To achieve this, we first define epistemic states to represent beliefs attached with strength, and then present a set of postulates to describe the change process on epistemic states that is determined by the strengths of input and establish representation theorems to characterize these postulates. As a result, we obtain a unique rewarding operator which is proved to be a merging operator that is in line with many other works. We also investigate existing postulates on belief merging and compare them with our postulates. In addition, we show that from an epistemic state, a corresponding ordinal conditional function by Spohn can be derived and the result of combining two epistemic states is thus reduced to the result of combining two corresponding ordinal conditional functions proposed by Laverny and Lang. Furthermore, when reduced to the belief revision situation, we prove that our results induce all the Darwiche and Pearl's postulates as well as the Recalcitrance postulate and the Independence postulate.