Domain-specific preferences for causal reasoning and planning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 it