belief revision

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belief revision

(artificial intelligence)
The area of theory change in which preservation of the information in the theory to be changed plays a key role.

A fundamental issue in belief revision is how to decide what information to retract in order to maintain consistency, when the addition of a new belief to a theory would make it inconsistent. Usually, an ordering on the sentences of the theory is used to determine priorities among sentences, so that those with lower priority can be retracted. This ordering can be difficult to generate and maintain.

The postulates of the AGM Theory for Belief Revision describe minimal properties a revision process should have.

References in periodicals archive ?
Asare (1992), that includes cognitive brain mapping and testing of the belief revision theory (Hogarth & Einhorn, 1992), seeking to answer the following research question: to what extent does brain mapping patterns follow behavioral patterns of auditors and accountants' judgments when assessing sequential evidences for going concern decisions?
The contributions that make up the main body of the text are devoted to negativity bias in investorsAE reactions to board of directorsAE risk oversight disclosure; the effects of Guanxi and compensation structure on the objectivity of Chinese internal auditors; variance reporting, belief revision, and profitability, and a wide variety of other related subjects.
The effects of audit task on evidence integration and belief revision.
The next chapter shows how Thagard's theory of belief revision applies to the issue of global warming by explaining how scientists come to accept--or not accept in the case of a few scientists and a large number of people in business and politics--the conclusions of climate science.
His explanatory coherence approach demonstrates the influence of emotional coherence on belief revision, or rather, resistance to revision.
An Analysis of Explanation as a Belief Revision Operation.
Topics discussed include representing case variations for learning general and specific adaptation rules, a theorem prover with dependent types of reasoning about actions, probabilistic association rules for item-based recommender systems, semantics for containment belief revision in the case of consistent complete theories, integrating individual and social intelligence into module-based agents without a central coordinator, improving batch reinforcement learning performance through transfer of samples, managing risk in recurrent auctions for robust resource allocation, and domain-dependent view of multiple robots path planning.
The first mechanism computed the new evidence and is a key feature of the belief revision model that frequency information should be computed as a weighted [DELTA]D:
Before believe changes the belief status of a proposition p, it performs a limited form of belief revision (Alchourron, Gardenfors, and Makinson 1985).
These findings add new-evidence to the growing body of data showing that human causal learning depends on the action of several mechanisms, as proposed by the Belief Revision Model.
Amongst the many subjects discussed are: the revision theory of truth and applications of revision rules, partiality and fixed point constructions, substitutional quantification, fuzzy logic, negation, belief revision, context dependence, hierarchies, Tarski on truth, deflationism, correspondence theories of truth, and normative aspects of truth.
Finally it presents the notion of plausibility and its application in belief revision systems.