Performance robustness of AI planners in the 2014 International Planning Competition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Solver competitions have been used in many areas of AI to assess the current state of the art and guide future research and development. AI planning is no exception, and the International Planning Competition (IPC) has been frequently run for nearly two decades. Due to the organisational and comput ational burden involved in running these competitions, solvers are generally compared using a single homogeneous hardware and software environment for all competitors. To what extent does the specific choice of hardware and software environment have an effect on solver performance, and is that effect distributed equally across the competing solvers? In this work, we use the competing planners and benchmark instance sets from the 2014 IPC to investigate these two questions. We recreate the 2014 IPC Optimal and Agile tracks on two distinct hardware environments and eight distinct software environments. We show that solver performance varies significantly based on the hardware and software environment, and that this variation is not equal for all planners. Furthermore, the observed variation is sufficient to change the competition rankings, including the top-ranked planners for some tracks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it