Descriptor Revision

Hansson, Sven Ove

doi:10.1007/s11225-013-9512-5

Cited by 21 publications

(4 citation statements)

References 15 publications

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“…A high-level implementation using Abstract State Machines (Börger and Stärk 2003) and providing the core functionality of a series of belief change operations is presented in (Beierle, Kern-Isberner, and Koch 2008;. The Java-based implementation of belief change operations given in (Haldimann, Beierle, and Kern-Isberner 2021) also provides an implementation of crevision because c-revision can be viewed as a special case of conditional descriptor revision (Hansson 2014).…”

Section: Formal and Semantic Basis Of Belief Revisionmentioning

confidence: 99%

Implementing Bounded Revision via Lexicographic Revision and C-revision

Sezgin

Kern-Isberner

2023

AAAI

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New information in the context of real life settings usually is accompanied by some kind of supplementary information that indicates context, reliability, or expertise of the information's source. Bounded Revision (BR) displays an iterated belief revision mechanism that takes as input a new information accompanied by a reference sentence acting as supplementary information, which specifies the depth with which the new input shall be integrated in the posterior belief state. The reference sentence specifies which worlds in the prior belief state are affected by the change mechanism. We show that Bounded Revision can be characterized by three simple, yet elegant postulates and corresponds to a special case of a lexicographic revision, which inherits all relevant features of BR. Furthermore, we present methodological implementations of BR including conditional revision with c-revisions, making it directly usable for conditional revision tools.

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Section: Formal and Semantic Basis Of Belief Revisionmentioning

confidence: 99%

Implementing Bounded Revision via Lexicographic Revision and C-revision

Sezgin

Kern-Isberner

2023

AAAI

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“…Solving these CSPs has been achieved by a constraint logic programming approach [5], employing the contraint solver provided by SICStus prolog [6]. An extension of these implementations to Hansson's descriptor revision [10] instantiated to a conditional logic is presented in [20], underlying the feasibility of implementing c-revsions by using a solver for CSPs because revisions can be simulated straightforwardly within the framework of descriptor revision. Moreover, having selection strategies at hand will be fruitful in such an implementation.…”

Section: Proposition 5 If σ Satisfies (Ip-esp σ ) Then * σ Satisfies (Tv)mentioning

confidence: 99%

Ranking kinematics for revising by contextual information

Sezgin

Kern-Isberner

Beierle

2021

Ann Math Artif Intell

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Probability kinematics is a leading paradigm in probabilistic belief change. It is based on the idea that conditional beliefs should be independent from changes of their antecedents’ probabilities. In this paper, we propose a re-interpretation of this paradigm for Spohn’s ranking functions which we call Generalized Ranking Kinematics as a new principle for iterated belief revision of ranking functions by sets of conditional beliefs with respect to their specific subcontext. By taking into account semantical independencies, we can reduce the complexity of the revision task to local contexts. We show that global belief revision can be set up from revisions on the local contexts via a merging operator. Furthermore, we formalize a variant of the Ramsey-Test based on the idea of local contexts which connects conditional and propositional revision in a straightforward way. We extend the belief change methodology of c-revisions to strategic c-revisions which will serve as a proof of concept.

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“…Since it also has the property that K X for all X ∈ X, there is no X with X K .) From blockage revision to relational revision: It was shown in Hansson (2014a) that • is a relational descriptor revision if it satisfies extensionality (If then K • = K • ), closure (K • = Cn(K • )), relative success (K • or K • = K ), regularity, cumulativity, and confirmation. It follows straight-forwardly from Definition 1 that the first three of these conditions are satisfied.…”

Section: Proof Of Theorem 1: From Regularity To Peripheral Non-occlusmentioning

confidence: 99%

“…The repertoire is a set of belief sets and will be denoted X. Hence, K • B p is assumed to be an element X of X such that X B p. In (Hansson 2014a) two ways to construct a descriptor revision • were investigated:…”

Section: Introductionmentioning

confidence: 99%

Blockage Revision

Hansson

2015

J of Log Lang and Inf

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Blockage revision is a version of descriptor revision, i.e. belief change in which a belief set K is changed with inputs whose success conditions are metalinguistic expressions containing the belief predicate B. This is a highly general framework that allows a single revision operator • to take inputs corresponding to sentential revision (K • B p), contraction (K • ¬B p) as well as more complex and composite operations. In blockage revision, such an operation is based on a relation of blockage among the set of potential outcomes. X Y signifies that if X satisfies the success condition of a belief change, then Y cannot be its outcome. The properties of blockage revision are investigated, and conditions on the blocking relation are specified that characterize various properties of the resulting operation of change.

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Descriptor Revision

Cited by 21 publications

References 15 publications

Implementing Bounded Revision via Lexicographic Revision and C-revision

Implementing Bounded Revision via Lexicographic Revision and C-revision

Ranking kinematics for revising by contextual information

Blockage Revision

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