Domain-specific preferences for causal reasoning and planning
Bibliographic record
Abstract
We address the issue of incorporating domain-specific prefer-ences in planning systems, where a preference may be seen as a “soft ” constraint that it is desirable, but not necessary, to sat-isfy. To this end, we identify two types of preferences, choice preferences that give a preference over which formulas (typi-cally subgoals) to establish, and temporal preferences, which specify a desirable ordering on the establishment of formu-las. Preferences may be constructed from actions or fluents but, as we show, this distinction is immaterial. In fact, we al-low preferences on arbitrary formulas build from action and fluent names. These preference orderings induce preference ordering on resulting plans, the maximal elements of which yield the preferred plans. We argue that the approach is gen-eral and flexible; as well, it handles conditional preferences. Our framework is developed in the context of transition sys-tems; hence, it is applicable to a large number of different action languages, including the well-known language C. Fur-thermore, our results are applicable to general planning for-malisms.
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 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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".