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Record W41899231

Conservative belief change

2004· article· en· W41899231 on OpenAlexaff
James P. Delgrande, Abhaya Nayak, Maurice Pagnucco

Bibliographic record

VenueNational Conference on Artificial Intelligence · 2004
Typearticle
Languageen
FieldComputer Science
TopicLogic, Reasoning, and Knowledge
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsBelief revisionPropositionContext (archaeology)Interpretation (philosophy)Belief structureState (computer science)Computer scienceSet (abstract data type)Representation (politics)MathematicsArtificial intelligenceEpistemologyAlgorithmPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

A standard assumption underlying traditional accounts of belief change is the principle of minimal change, that an agent's belief state should be modified minimany to incorporate new information. In this paper we introduce a novel account of belief change in which the agent's belief state is modified minimally to incorporate exactly the new information. Thus a revision by p ??? q will result in a new belief state in which p ??? q is believed, but a stronger proposition (such as p Λ q) is not, regardless of the initial form of the belief state. This form of belief change is termed conservative belief change and corresponds to a Gricean interpretation of the input formula. We investigate belief revision in this framework, and provide a representation result between a set of postulates characterising this form of belief change and a construction in terms of systems of spheres. This approach is extended to that of belief revision with respect to a specified context. Last, we show how this approach resolves a longstanding problem in belief revision.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0020.010
Scholarly communication0.0050.009
Open science0.0020.005
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.250
GPT teacher head0.358
Teacher spread0.108 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2004
Admission routes1
Has abstractyes

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