Partial pathfinding using map abstraction and refinement
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
Classical search algorithms such as A* or IDA* are useful for computing optimal solutions in a single pass, which can then be executed. But in many domains agents either do not have the time to compute complete plans before acting, or should not spend the time to do so, due to the dynamic nature of the environment. Extensions to A* such as LRTA* address this problem by gradually learning an exact heuristic function, but the learning process is quite slow. In this paper we introduce Partial-Refinement A* (PRA*), which can fully interleave planning and acting through path abstraction and refinement. We demonstrate the etfectiveness of PRA* in the domain of real-time strategy (RTS) games. In maps taken from popular RTS games. we show that PRA* is not only able to cleanly interleave planning and execution. but it is also able to do so with only minimal losses of optimality.
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 | 0.001 | 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 | 0.000 | 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