Action Schema Networks for Numerical Planning
Why this work is in the frame
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Bibliographic record
Abstract
<p>Planning is the fundamental ability of an intelligent agent to reason about what decisions it should make in a given environment to achieve a certain set of goals. Action Schema Networks (ASNet) find generalized policies for classical planning problems. In this thesis, we extend ASNet to work with numerical planning problems. We present the technique to propositionalize numerical variables to convert them from continuous infinite ranges to a finite domain. We use a non-generalized numerical planner, ENHSP, to teach ASNet to solve numerical planning problems by learning to mimic the actions chosen by this teacher planner for problem instances. We have optimized the training algorithm with action generation techniques, objective functions, and evaluation strategies. ASNet finds a generalized policy and weights after training which allows it to share these policy and weights to solve unseen problem instances of the same domain. We analyze our approach through an extensive experimental study aimed at evaluating the performance of ASNet on several numerical planning domains. The results show that our numerical ASNet can effectively handle many numerical planning domains and significantly outperforms the baseline planner in terms of execution time. This work is a first step to applying neural networks to numerical generalized planning.</p>
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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