Updating logical databases
Unifying default reasoning and belief revision in a modal framework
Artificial Intelligence
Reasoning about knowledge
Qualitative probabilities for default reasoning, belief revision, and causal modeling
Artificial Intelligence
On the logic of iterated belief revision
Artificial Intelligence
Modeling beliefs in dynamic systems
Modeling beliefs in dynamic systems
Modeling belief in dynamic systems, part I: foundations
Artificial Intelligence
A unified model of qualitative belief change: a dynamical systems perspective
Artificial Intelligence
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Generalized update: belief change in dynamic settings
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Belief revision with uncertain inputs in the possibilistic setting
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
A qualitative Markov assumption and its implications for belief change
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Journal of Logic, Language and Information
Filtering vs Revision and Update: Let Us Debate!
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Revising aggregation hierarchies in OLAP: a rule-based approach
Data & Knowledge Engineering - Special issue: Advances in OLAP
On merging user-supplied topic ontologies on the semantic web
Proceedings of the 44th annual Southeast regional conference
Outlier detection by logic programming
ACM Transactions on Computational Logic (TOCL)
Modeling belief in dynamic systems part II: revision and update
Journal of Artificial Intelligence Research
Plausibility measures: a general approach for representing uncertainty
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
From knowledge-based programs to graded belief-based programs, part II: off-line reasoning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A framework for managing uncertain inputs: An axiomization of rewarding
International Journal of Approximate Reasoning
ICCS'06 Proceedings of the 14th international conference on Conceptual Structures: inspiration and Application
Reconfigurable web service composition using belief revision
ADCONS'11 Proceedings of the 2011 international conference on Advanced Computing, Networking and Security
| Hi-index | 0.00 |
Research in belief revision has been dominated by work that lies firmly within the classic AGM paradigm, characterized by a well-known set of postulates governing the behavior of "rational" revision functions. A postulate that is rarely criticized is the success postulate: the result of revising by an observed proposition 驴 results in belief in 驴. This postulate, however, is often undesirable in settings where an agent's observations may be imprecise or noisy. We propose a semantics that captures a new ontology for studying revision functions, which can handle noisy observations in a natural way while retaining the classical AGM model as a special case. We present a characterization theorem for our semantics, and describe a number of natural special cases that allow ease of specification and reasoning with revision functions. In particular, by making the Markov assumption, we can easily specify and reason about revision.