Bibliographic record
Abstract
Incomplete single-agent search methods are often better suited to real-time pathfinding tasks than complete methods (such as A*). Incomplete methods conduct a limited-depth lookahead search, i.e., expand a part of the space centered on the agent, and heuristically evaluate the distances from the frontier of the expanded space to the goal. Actions selected this way are not necessarily optimal, but it is generally believed that deeper lookahead increases the quality of decisions. However, in two-player games, where similar methods are used, it has long been known that this is not always the case [7, 1]. This phenomenon has been termed minimax pathology. More recently pathological behavior was discovered in single-agent search as well [3]. Some attempts to explain it have been made [5, 6], but the pathology in single-agent search is largely still not understood. In this paper we investigate lookahead pathology in real-time pathfinding on maps from commercial computer games. First, we present an empirical study showing a degree of pathology in over 90% of the problems considered. Second, we give four explanations for such wide-spread pathological behavior.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".