A program-level approach to revising logic programs under the answer set semantics
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Bibliographic record
Abstract
Abstract An approach to the revision of logic programs under the answer set semantics is presented. For programs P and Q , the goal is to determine the answer sets that correspond to the revision of P by Q , denoted P * Q . A fundamental principle of classical (AGM) revision, and the one that guides the approach here, is the success postulate . In AGM revision, this stipulates that α ∈ K * α. By analogy with the success postulate, for programs P and Q , this means that the answer sets of Q will in some sense be contained in those of P * Q . The essential idea is that for P * Q , a three-valued answer set for Q , consisting of positive and negative literals, is first determined. The positive literals constitute a regular answer set, while the negated literals make up a minimal set of naf literals required to produce the answer set from Q . These literals are propagated to the program P , along with those rules of Q that are not decided by these literals. The approach differs from work in update logic programs in two main respects. First, we ensure that the revising logic program has higher priority, and so we satisfy the success postulate; second, for the preference implicit in a revision P * Q , the program Q as a whole takes precedence over P , unlike update logic programs, since answer sets of Q are propagated to P . We show that a core group of the AGM postulates are satisfied, as are the postulates that have been proposed for update logic programs.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it