Bibliographic record
Abstract
We design a representation based on the situation calculus to facilitate development, maintenance and elaboration of very large taxonomies of actions. This representation leads to more compact and modular basic action theories (BATs) for reasoning about actions than currently possible. We compare our representation with Reiter’s BATs and prove that our rep-resentation inherits all useful properties of his BATs. More-over, we show that our axioms can be more succinct, but ex-tended Reiter’s regression can still be used to solve the projec-tion problem (this is the problem of whether a given logical expression will hold after executing a sequence of actions). We also show that our representation has significant compu-tational advantages. For taxonomies of actions that can be represented as finitely branching trees, the regression oper-ator can work exponentially faster with our theories than it works with Reiter’s BATs. Finally, we propose general guide-lines on how a taxonomy of actions can be constructed from the given set of effect axioms in a domain.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".