Fast Synthesis of Algebraic Heuristic Functions for Video-game Pathfinding
Bibliographic record
Abstract
Heuristic search is widely used in games for pathfinding and general planning. High-quality heuristic functions are key to finding a low-cost solution quickly. Commonly used heuristic functions for video-game pathfinding are either manually designed and generic or pre-computed for a specific map. The former fail to take advantage of pathfinding specifics while the latter tend to have a large memory footprint, may require substantial pre-computation and are not portable to other maps or easily presentable to humans. In this work we attempt to combine the best of both approaches by automatically synthesizing well performing pathfinding-specific yet compact and human-readable heuristics. We do so by defining a space of algebraic formulae expressing heuristic functions and then conducting an automated search of the space. To make the synthesis tractable we employ a multi-tier evaluation which allows us to quickly filter out low-quality heuristics while saving time to more thoroughly evaluate better ones. Such triage of candidate heuristics enables us to synthesize compact heuristics that outperform the standard baseline on video-game pathfinding benchmarks. By then adding the synthesized heuristics back to the synthesis space we show that synthesis on new maps can be substantially sped up to merely few minutes per 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.006 | 0.001 |
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".