Planning with first-order temporally extended goals using heuristic search
Why this work is in the frame
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Bibliographic record
Abstract
Temporally extended goals (TEGs) refer to properties that must hold over intermediate and/or final states of a plan. The problem of planning with TEGs is of renewed interest because it is at the core of planning with temporal prefer-ences. Currently, the fastest domain-independent classical planners employ some kind of heuristic search. However, ex-isting planners for TEGs are not heuristic and are only able to prune the search space by progressing the TEG. In this paper we propose a method for planning with TEGs using heuris-tic search. We represent TEGs using a rich and compelling subset of a first-order linear temporal logic. We translate a planning problem with TEGs to a classical planning prob-lem. With this translation in hand, we exploit heuristic search to determine a plan. Our translation relies on the construc-tion of a parameterized nondeterministic finite automaton for the TEG. We have proven the correctness of our algorithm and analyzed the complexity of the resulting representation. The translator is fully implemented and available. Our ap-proach consistently outperforms TLPLAN on standard bench-mark domains, often by orders of magnitude. 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 | 0.001 |
| 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 | 0.000 |
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