Synthesizing Priority Planning Formulae for Multi-Agent Pathfinding
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
Prioritized planning is a popular approach to multi-agent pathfinding. It prioritizes the agents and then repeatedly invokes a single-agent pathfinding algorithm for each agent such that it avoids the paths of higher-priority agents. Performance of prioritized planning depends critically on cleverly ordering the agents. Such an ordering is provided by a priority function. Recent work successfully used machine learning to automatically produce such a priority function given good orderings as the training data. In this paper we explore a different technique for synthesizing priority functions, namely program synthesis in the space of arithmetic formulae. We synthesize priority functions expressed as arithmetic formulae over a set of meaningful problem features via a genetic search in the space induced by a context-free grammar. Furthermore we regularize the fitness function by formula length to synthesize short, human-readable formulae. Such readability is an advantage over previous numeric machine-learning methods and may help explain the importance of features and how to combine them into a good priority function for a given domain. Moreover, our experimental results show that our formula-based priority functions outperform existing machine-learning methods on the standard benchmarks in terms of success rate, run time and solution quality without using more training data.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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