A situation-calculus semantics for an expressive fragment of PDDL
Bibliographic record
Abstract
The Planning Domain Definition Language (PDDL) has be-come a common language to specify planning problems, fa-cilitating the formulation of benchmarks and a direct com-parison of planners. Over the years PDDL has been extended beyond STRIPS and ADL in various directions, for exam-ple, by adding time and concurrent actions. The current se-mantics of PDDL is purely meta-theoretic and quite complex, which makes an analysis difficult. Moreover, relating the lan-guage to other action formalisms is also nontrivial. We pro-pose an alternative semantics for an expressive fragment of PDDL within the situation calculus. This yields at least two advantages. For one, the new semantics is purely declara-tive, making it amenable to an analysis in terms of logical entailments. For another, it facilitates the comparison with and mapping to other formalisms that are defined on top of the same logic, such as the agent control language Golog. In particular we obtain the semantical foundation for embedding efficient PDDL-based planners into the more expressive, yet computationally expensive Golog, thus combining the ben-efits of both. Other by-products of our investigations are a simpler account of durative actions in the situation calculus and a new notion of compulsory actions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".