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Two views of belief: belief as generalized probability and belief as evidence
Artificial Intelligence
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ECSQARU/FAPR '97 Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning
Updating sets of probabilities
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
On a Linear Representation Theory for Quantitative Belief Change
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Artificial Intelligence
Belief Revision through Forgetting Conditionals in Conditional Probabilistic Logic Programs
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Revising imprecise probabilistic beliefs in the framework of probabilistic logic programming
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
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The AGM theory of belief revision has become an important paradigm for investigating rational belief changes. Unfortunately, researchers working in this paradigm have restricted much of their attention to rather simple representations of belief states, namely logically closed sets of propositional sentences. In our opinion, this has resulted in a too abstract categorisation of belief change operations: expansion, revision, or contraction. Occasionally, in the AGM paradigm, also probabilistic belief changes have been considered, and it is widely accepted that the probabilistic version of expansion is conditioning. However, we argue that it may be more correct to view conditioning and expansion as two essentially different kinds of belief change, and that what we call constraining is a better candidate for being considered probabilistic expansion.