1,252 research outputs found
AGM 25 years: twenty-five years of research in belief change
The 1985 paper by Carlos Alchourrón (1931–1996), Peter Gärdenfors,
and David Makinson (AGM), “On the Logic of Theory Change: Partial Meet
Contraction and Revision Functions” was the starting-point of a large and
rapidly growing literature that employs formal models in the investigation
of changes in belief states and databases. In this review, the first twenty five years of this development are summarized. The topics covered include
equivalent characterizations of AGM operations, extended representations of
the belief states, change operators not included in the original framework,
iterated change, applications of the model, its connections with other formal
frameworks, computatibility of AGM operations, and criticism of the model.info:eu-repo/semantics/publishedVersio
Trust as a precursor to belief revision
Belief revision is concerned with incorporating new information into a pre-existing set of beliefs. When the new information comes from another agent, we must first determine if that agent should be trusted. In this paper, we define trust as a pre-processing step before revision. We emphasize that trust in an agent is often restricted to a particular domain of expertise. We demonstrate that this form of trust can be captured by associating a state partition with each agent, then relativizing all reports to this partition before revising. We position the resulting family of trust-sensitive revision operators within the class of selective revision operators of Ferme and Hansson, and we prove a representation result that characterizes the class of trust-sensitive revision operators in terms of a set of postulates. We also show that trust-sensitive revision is manipulable, in the sense that agents can sometimes have incentive to pass on misleading information
Generalized belief change with imprecise probabilities and graphical models
We provide a theoretical investigation of probabilistic belief revision in complex frameworks, under extended conditions of uncertainty, inconsistency and imprecision. We motivate our kinematical approach by specializing our discussion to probabilistic reasoning with graphical models, whose modular representation allows for efficient inference. Most results in this direction are derived from the relevant work of Chan and Darwiche (2005), that first proved the inter-reducibility of virtual and probabilistic evidence. Such forms of information, deeply distinct in their meaning, are extended to the conditional and imprecise frameworks, allowing further generalizations, e.g. to experts' qualitative assessments. Belief aggregation and iterated revision of a rational agent's belief are also explored
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