Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Uncertainty and vagueness in knowledge based systems
Uncertainty and vagueness in knowledge based systems
Propositional knowledge base revision and minimal change
Artificial Intelligence
A method for updating that justifies minimum cross entropy
International Journal of Approximate Reasoning
Handbook of logic in artificial intelligence and logic programming (vol. 3)
The uncertain reasoner's companion: a mathematical perspective
The uncertain reasoner's companion: a mathematical perspective
Qualitative probabilities for default reasoning, belief revision, and causal modeling
Artificial Intelligence
On the logic of iterated belief revision
Artificial Intelligence
Nonmonotonic reasoning, conditional objects and possibility theory
Artificial Intelligence
Characterizing the principle of minimum cross-entropy within a conditional-logical framework
Artificial Intelligence
Belief functions and default reasoning
Artificial Intelligence
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
A Maximum Entropy Approach to Nonmonotonic Reasoning
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Principle of Conditional Preservation in Belief Revision
FoIKS '02 Proceedings of the Second International Symposium on Foundations of Information and Knowledge Systems
Representing and Learning Conditional Information in Possibility Theory
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Postulates for Conditional Belief Revision
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
A Consistency-Based Model for Belief Change: Preliminary Report
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Relations between the logic of theory change and nonmonotonic logic
Proceedings of the Workshop on The Logic of Theory Change
Modelling conditional knowledge discovery and belief revision by abstract state machines
ASM'03 Proceedings of the abstract state machines 10th international conference on Advances in theory and practice
Conditionals in nonmonotonic reasoning and belief revision: considering conditionals as agents
Conditionals in nonmonotonic reasoning and belief revision: considering conditionals as agents
Plausibility measures and default reasoning
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Computational methods for database repair by signed formulae*
Annals of Mathematics and Artificial Intelligence
IJCAR '08 Proceedings of the 4th international joint conference on Automated Reasoning
A Verified AsmL Implementation of Belief Revision
ABZ '08 Proceedings of the 1st international conference on Abstract State Machines, B and Z
Qualitative Knowledge Discovery
Semantics in Data and Knowledge Bases
A conceptual agent model based on a uniform approach to various belief operations
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
A framework for managing uncertain inputs: An axiomization of rewarding
International Journal of Approximate Reasoning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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Although the crucial role of if-then-conditionals for the dynamics of knowledge has been known for several decades, they do not seem to fit well in the framework of classical belief revision theory. In particular, the propositional paradigm of minimal change guiding the AGM-postulates of belief revision proved to be inadequate for preserving conditional beliefs under revision. In this paper, we present a thorough axiomatization of a principle of conditional preservation in a very general framework, considering the revision of epistemic states by sets of conditionals. This axiomatization is based on a nonstandard approach to conditionals, which focuses on their dynamic aspects, and uses the newly introduced notion of conditional valuation functions as representations of epistemic states. In this way, probabilistic revision as well as possibilistic revision and the revision of ranking functions can all be dealt with within one framework. Moreover, we show that our approach can also be applied in a merely qualitative environment, extending AGM-style revision to properly handling conditional beliefs.