Dynamic control in path-planning with real-time heuristic search
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
Real-time heuristic search methods, such as LRTA*, are used by situated agents in applications that require the amount of planning per action to be constant-bounded regardless of the problem size. LRTA * interleaves planning and execution, with a fixed search depth being used to achieve progress to-wards a fixed goal. Here we generalize the algorithm to allow for a dynamically changing search depth and a dynamically changing (sub-)goal. Evaluation in path-planning on video-game maps shows that the new algorithm significantly outper-forms fixed-depth, fixed-goal LRTA*. The new algorithm can achieve the same quality solutions as LRTA*, but with nine times less computation, or use the same amount of computa-tion, but produce four times better quality solutions. These extensions make real-time heuristic search a practical choice for path-planning in computer video-games.
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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 | 0.001 | 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 | 0.001 | 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