ArvandHerd: Parallel Planning with a Portfolio
Bibliographic record
Abstract
ArvandHerd is a parallel planner that won the multicore sequential satisficing track of the 2011 International Planning Competition (IPC 2011). It assigns processors to run different members of an algorithm portfolio which contains several configurations of each of two different planners: LAMA-2008 and Arvand. In this paper, we demonstrate that simple techniques for using different planner configurations can significantly improve the coverage of both of these planners. We then show that these two planners, when using multiple configurations, can be combined to construct a high performance parallel planner. In particular, we will show that ArvandHerd can solve more IPC benchmark problems than even a perfect parallelization of LAMA-2011, which won the satisficing track at IPC 2011. We will also show that the coverage of ArvandHerd can be further improved if LAMA-2008 is replaced by LAMA-2011 in the portfolio.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".