Finite LTL Synthesis as Planning
Bibliographic record
Abstract
LTL synthesis is the task of generating a strategy that satisfies a Linear Temporal Logic (LTL) specification interpreted over infinite traces. In this paper we examine the problem of LTLf synthesis, a variant of LTL synthesis where the specification of the behaviour of the strategy we generate is interpreted over finite traces -- similar to the assumption we make in many planning problems, and important for the synthesis of business processes and other system interactions of finite duration. Existing approaches to LTLf synthesis transform LTLf into deterministic finite-state automata (DFA) and reduce the synthesis problem to a DFA game. Unfortunately, the DFA transformation is worst-case double-exponential in the size of the formula, presenting a computational bottleneck. In contrast, our approach exploits non-deterministic automata, and we reduce the synthesis problem to a non-deterministic planning problem. We leverage our approach not only for strategy generation but also to generate certificates of unrealizability -- the first such method for LTLf. We employ a battery of techniques that exploit the structure of the LTLf specification to improve the efficiency of our transformation to automata. We combine these techniques with lazy determinization of automata and on-the-fly state abstraction. We illustrate the effectiveness of our approach on a set of established LTL synthesis benchmarks adapted to finite LTL.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".