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Record W2001413564 · doi:10.1109/coginf.2010.5599734

A solution to commutativity problems found in iterated belief revision

2010· article· en· W2001413564 on OpenAlexaff
Yan Ma, Robert E. Mercer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicLogic, Reasoning, and Knowledge
Canadian institutionsWestern University
Fundersnot available
KeywordsCommutative propertyIterated functionBelief revisionSelection (genetic algorithm)State (computer science)MathematicsIntrusionComputer scienceMathematical economicsAlgorithmEpistemologyArtificial intelligenceDiscrete mathematicsPhilosophy

Abstract

fetched live from OpenAlex

This paper mainly addresses the commutativity issue discovered in iterated belief revisions under the framework of AGM. Namely, given an iterated formula K ∗ x ∗ y, under what condition K ∗ x ∗ y = K ∗ (x ⁁ y) = K ∗ y ∗ x can hold needs to be reconsidered. As we shall demonstrate, AGM 7th and 8th postulates together falsely imply the commutativity existing in Rott's selection puzzle. On the other hand, Nayak et al.'s counteracting problem which demands commutativity is rejected by AGM postulates. The worse thing is the two realistic puzzles cannot be correctly simulated by AGM postulates under classic logics. Towards these issues, we suggest the inaccurate definition of consequence operation C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</inf> needs to be modified in order to accommodate part of Rott's puzzle. Furthermore, we illustrated neither of the two puzzles can be simulated under the AGM framework in the content level. Therefore, we present our approach as follows: first, we built a belief revision framework which satisfies the first six AGM postulates in the content level. Second, by setting appropriate OCF, our framework is able to accommodate the two puzzles in epistemic state level. Third, we prove the commutativity can be held in the epistemic state level if and only if (K ∗ x) ∩ (K ∗ y) ≠ φ and (K ∗ x) ∗ (K ∗ y) ├/┴ and thus eliminate the intrusion by the selection puzzle. Last, we demonstrate the hold of commutativity is only a special case of the selection criterion we suggest, that is, the most explainable interpretation(s) that satisfy the most recent evidence should be chosen to be the final revision result. In this sense, we can tell in the counteracting problems, namely, when only (K ∗ x) ∩ (K ∗ y) ≠ φ is satisfied, why the revision result had better be represented by K ∗ (x ∗ y) is because the interpretation(s) suggested in our selection criterion is only guaranteed to be in K ∗ (x ∗ y) rather than its iterated form. Hence, we unify both of puzzles with the modified AGM framework.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.688
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.268
Teacher spread0.250 · 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 teacher head, 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

Citations0
Published2010
Admission routes1
Has abstractyes

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