On forgetting postulates in answer set programming
Bibliographic record
Abstract
Forgetting is an important mechanism for logic-based agent systems. A recent interest has been in the desirable properties of forgetting in answer set programming (ASP) and their impact on the design of forgetting operators. It is known that some sub-sets of these properties are incompatible, i.e., they cannot be satisfied at the same time. In this paper, we are interested in the question on the largest set ∆ of pairs (Π, V), where Π is a logic program and V is a set of atoms, such that a forgetting operator ex-ists that satisfies all the desirable properties for each (Π, V) in ∆. We answer this question positively by discovering the precise condition under which the knowledge forgetting, a well-established approach to forgetting in ASP, satisfies the property of strong persistence, which leads to a sufficient and neces-sary condition for a forgetting operator to satisfy all the desirable properties proposed in the literature. We explore computational complexities on check-ing the condition and present a syntactic character-ization which can serve as the basis of computing knowledge forgetting in ASP.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".