Better Time Constrained Search via Randomization and Postprocessing
Bibliographic record
Abstract
Most of the satisficing planners which are based on heuristic search iteratively improve their solution quality through an anytime approach. Typically, the lowest-cost solution found so far is used to constrain the search. This avoids areas of the state space which cannot directly lead to lower cost so-lutions. However, in conjunction with a post-processing plan improvement system such as ARAS, this bounding approach can harm a planner’s performance. The new anytime search framework of Diverse Any-Time Search avoids this behaviour by taking advantage of the fact that post-processing can often improve a lower quality input plan to a superior final plan. The framework encourages di-versity of “raw ” plans by restarting and using randomization, and does not use previous solutions for bounding. This gives a post-processing system a more diverse set of plans to work on, which improves performance. When adding both Diverse Any-Time Search and the ARAS post-processor to LAMA-2011, the winner of the most recent IPC planning competi-tion, and AEES, the Anytime Explicit Estimation Algorithm, the performance on the 550 IPC 2008 and IPC 2011 problems is improved by almost 60 points according to the IPC metric, from 511 to over 570 on LAMA-2011, and 73 points from 440 to over 513 on AEES.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".