Game-map Pathfinding with Per-Problem Selection of Synthesized Heuristics
Bibliographic record
Abstract
Variants of A* search are widely used for video-game pathfinding with a heuristic function that is typically either a generic formula designed by humans (e.g., the Manhattan distance) or pre-computed for a specific video-game map. Recent work attempted to combine portability of the former and higher performance of the latter by automatically synthesizing arithmetic formulae. Such formulae are simple enough to be human-readable, portable enough to provide guidance on novel maps and yet complex enough to notably outperform a baseline. Each formula-represented heuristic was synthesized for a given map, presumably capturing some features of the map. However, maps can be non-uniform and some regions of one map may have features similar to another map. This work uses a portfolio of synthesized heuristics and selects from it on a per-problem basis. The selection is done automatically by determining the pair of map regions in which the start and the goal states of a given problem instance belong. A pre-computed database gives the highest-performing heuristic from the portfolio for that pair of regions. This heuristic is then used to guide A* to solve the problem instance. Empirical evaluation on maps from video games indicates noticeable speed-up compared to using a single synthesized heuristic for all problem instances on a map.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".