Any Angle Path Finding in Stochastic Obstacle Scenes
Bibliographic record
Abstract
Path planning with stochastic obstacles is well known researching area. The Canadian traveler problem (CTP) is a challenging stochastic optimization problem of traversing in a given graph having blocked edges and the disambiguation status of these edges can be settled with predefined probabilities. Discretized version of stochastic obstacle scene problem (D-SOSP) is most commonly used variant of CTP. The objective is to design a travel plan that would guarantee the shortest path including the obstacle disambiguation cost. In this work, we present Any-Angle (ANYA) path finding in discretized stochastic obstacle scenes using the exact algorithm AO* with caching (CAO*). The admissible upper bounds in the CAO* are found by making use of Dijkstra's shortest path. However, ANYA algorithm, being recently proposed, is already shown to outperform shortest path algorithms by investigating the interval sets. Our methodology is exhibited distinctly via computational examples involving a data map of navy forces minefield.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".