Translating PDDL into CSP# - The PAT Approach
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
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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it