Beyond classical planning: procedural control knowledge and preferences in state-of-the-art planners
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.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".