Exploiting procedural domain control knowledge in state-of-the-art planners
Bibliographic record
Abstract
Domain control knowledge (DCK) has proven effective in improving the efficiency of plan generation by reducing the search space for a plan. Procedural DCK is a compelling type of DCK that supports a natural specification of the skele-ton of a plan. Unfortunately, most state-of-the-art planners do not have the machinery necessary to exploit procedural DCK. To resolve this deficiency, we propose to compile procedural DCK directly into PDDL2.1, thus enabling any PDDL2.1-compatible planner to exploit it. The contribution of this pa-per is threefold. First, we propose a PDDL-based seman-tics for an Algol-like, procedural language that can be used to specify DCK in planning. Second, we provide a polyno-mial algorithm that translates an ADL planning instance and a DCK program, into an equivalent, program-free PDDL2.1 instance whose plans are only those that adhere to the pro-gram. Third, we argue that the resulting planning instance is well-suited to being solved by domain-independent heuris-tic planners. To this end, we propose three approaches to computing domain-independent heuristics for our translated instances, sometimes leveraging properties of our translation to guide search. In our experiments on familiar PDDL plan-ning benchmarks we show that the proposed compilation of procedural DCK can significantly speed up the performance of a heuristic search planner. Our translators are implemented and available on the web.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| 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".