Planning with temporally extended goals using heuristic search
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
Temporally extended goals (TEGs) refer to properties that must hold over intermediate and/or final states of a plan. Cur-rent planners for TEGs prune the search space during plan-ning via goal progression. However, the fastest classical domain-independent planners rely on heuristic search. In this paper we propose a method for planning with propositional TEGs using heuristic search. To this end, we translate an in-stance of a planning problem with TEGs into an equivalent classical planning problem. With this translation in hand, we exploit heuristic search to determine a plan. We represent TEGs using propositional linear temporal logic which is in-terpreted over finite sequences of states. Our translation is based on the construction of a nondeterministic finite automa-ton for the TEG. We prove the correctness of our algorithm and analyze the complexity of the resulting representation. The translator is fully implemented and available. Our ap-proach consistently outperforms existing approaches to plan-ning with TEGs, often by orders of magnitute. 1
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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