Improving Planning Performance Using Low-Conflict Relaxed Plans
Bibliographic record
Abstract
The FF relaxed plan heuristic is one of the most effective techniques in domain-independent satisficing planning and is used by many state-of-the-art heuristic-search planners. However, it may sometimes provide quite inaccurate information, since its relaxation strategy, which ignores the delete effects of actions, may oversimplify a problem's structure. In this paper, we propose a novel algorithm for computing relaxed plans which — although still relaxed — aim at respecting much of the structure of the original problem. We accomplish this by generating relaxed plans with a reduced number of conflicts. An action a will add a conflict when added to a relaxed plan if the resulting plan is provably illegal (i.e, not executable) in the un-relaxed problem. As a second contribution, we propose a new lookahead strategy, in the spirit of Vidal's YAHSP lookahead, that can better exploit the contents of relaxed plans. In our experimental analysis, we show that the resulting heuristic improves over the FF heuristic in a number of domains, most notably when lookahead is enabled. Moreover, the resulting system, which uses our new lookahead, is competitive with state-of-the-art planners, and even better in terms of the number of solved problems.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".