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 ?
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.
Thagard argues in favour of what he sees is a highly effective and computationally efficient way of modelling belief revision.
His explanatory coherence approach demonstrates the influence of emotional coherence on belief revision, or rather, resistance to revision.
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.
Finally it presents the notion of plausibility and its application in belief revision systems.
For a thorough presentation of the AGM model for belief revision we refer the reader to [Alchourron et al.
An "integrated" theory of belief revision is presented which proposes that belief perseverance (the contrast effect) is an increasing (decreasing) function of confidence in beliefs.
Recent behavioral accounting studies find that information order can affect the belief revision process.
The final part of the book gathers the contributions of two distinguished authors, best known for their contributions to the literatures on belief revision and non-monotonic reasoning.
Instead they show that a number of well entrenched principles of rational belief and belief revision do not apply to conditionals.
If we apply these concepts to belief revision, the postulates and the constructions should make no reference to the sentences of the initial set.
Although almost universally proclaimed by belief revision theorists, the paper argues that both versions of the principle are dogmas that are not in fact (and perhaps should not be) adhered to.