Translating PDDL into CSP# - The PAT Approach
Bibliographic record
Abstract
Model checking provides a way to automatically verify hardware and software systems, whereas the goal of planning is to produce a sequence of actions that leads from the initial state to the desired goal state. Recently research indicates that there is a strong connection between model checking and planning problem solving. In this paper, we investigate the feasibility of using a newly developed model checking framework, Process Analysis Toolkit (PAT), to serve as a planning solution provider for upper layer applications. We first carried out a number of experiments on different planning tools in order to compare their performance and capabilities. Our experimental results showed that the performance of the PAT model checker is comparable to that of state-of-art planners for certain categories of problems. We further propose a set of translation rules for mapping from a commonly used planning notation - PDDL into the CSP# modeling language of PAT. Finally, we provide evaluations on the translated models against other approaches in the planning domain to demonstrate the effectiveness of using the PAT model checker for planning.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".