A rational extension of stable model semantics to the full propositional language
Bibliographic record
Abstract
Answer set programming is the most appreciated framework for non-monotonic reasoning. Stable model semantics, as the semantics behind this success, has been subject to many extensions. The two main such extensions are equilibrium models and FLP semantics. Despite their very interesting foundations, they both have two problems: they cannot guarantee either minimality, or rationality of their intended models. That is, both these semantics allow models in which some atoms are self-justified (i.e., the only possible reason for including those atoms in the model are those atoms themselves). Present paper extends stable model semantics to the full propositional language while guaranteeing both properties above. Our extension is called supported because it guarantees the existence of noncircular justifications for all atoms in a supported model. These goals are achieved through a form of completion in intuitionistic logic. We also discuss how supported models relate to other semantics for non-monotonic reasoning such as equilibrium models. Finally, we discuss the complexity of reasoning about supported models and show that the complexity of brave/cautious reasoning in supported semantics remains as before, i.e., the rationality property comes for no additional cost.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".