Bibliographic record
Abstract
Real-time heuristic search methods are used when planning time available per agent's move is severely limited (e.g., while pathfinding in a video game). Such agents interleave planning and plan execution. As the agent has to move before a complete plan is computed, it is prone to be misguided by inaccuracies in its heuristic. To get out of heuristic depressions, such agents update their heuristic over time. The usual update process requires multiple state revisits which can make the agent appear irrational to the player. To alleviate such map "scrubbing" we propose a new learning mechanism inspired by the psychological notion of anxiety. Our agent maintains a level of anxiety which increases due to state revisits and decays naturally over time. Agent's anxiety causes it to update the heuristic more aggressively thus filling heuristic depressions quicker. Such anxiety-accelerated learning can be used on top of other real-time heuristic search techniques. Empirical evaluation on video-game pathfinding benchmarks demonstrates benefits for the average solution quality when the new mechanism is used by itself or in combination with expendable state marking.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.005 |
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 teacher head, 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".