Conditional irrelevance in belief change
Bibliographic record
Abstract
This thesis presents an approach to incorporating qualitative assertions of conditional irrelevance into belief change, in order to address the limitations of existing work which considers only unconditional irrelevance.These assertions serve to enforce the requirement of minimal change to existing beliefs, while also suggesting a route to reducing the computational cost of belief change by excluding irrelevant beliefs from consideration.Our approach uses modified multivalued dependencies to represent domain-dependent conditional irrelevance assertions.We consider these assertions as capturing a property of the underlying domain, and consequently assume that a knowledge engineer has specified a collection of conditional irrelevance assertions to be taken into account during belief change.We introduce two related notions of what it means for a conditional irrelevance assertion to be taken into account by a belief revision or contraction operator: partial and full compliance.We also show that partially (and fully) compliant belief revision and contraction operators are interdefinable via the Levi and Harper identities.Further, we provide characterisations of partially and fully compliant belief revision operators in terms of semantic conditions on their associated faithful rankings.Using these characterisations, we show that partially and fully compliant belief revision operators exist.Finally, we compare our approach to existing work on unconditional irrelevance in belief change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".