Beyond classical planning: procedural control knowledge and preferences in state-of-the-art planners
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
Real-world planning problems can require search over thou-sands of actions and may yield a multitude of plans of dif-fering quality. To solve such real-world planning problems, we need to exploit domain control knowledge that will prune the search space to a manageable size. And to ensure that the plans we generate are of high quality, we need to guide search towards generating plans in accordance with user pref-erences. Unfortunately, most state-of-the-art planners cannot exploit control knowledge, and most of those that can exploit user preferences require those preferences to only talk about the final state. Here, we report on a body of work that extends classical planning to incorporate procedural control knowl-edge and rich, temporally extended user preferences into the specification of the planning problem. Then to address the en-suing nonclassical planning problem, we propose a broadly-applicable compilation technique that enables a diversity of state-of-the-art planners to generate such plans without ad-ditional machinery. While our work is firmly rooted in AI planning it has broad applicability to a variety of computer science problems relating to dynamical systems.
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