Bibliographic record
Abstract
The standard situation calculus assumes that atomic actions are deterministic. But many domains involve nondeterministic actions, with problems such as fully observable nondeterministic (FOND) planning and high-level program execution requiring solutions. Various approaches have been proposed to accommodate nondeterminism on top of the standard situation calculus language, for instance by introducing nondeterministic programs as in Golog and ConGolog. But a key problem in these approaches is that they don’t clearly distinguish between choices that can be made by the agent and choices that are made by the environment, i.e., angelic vs. devilish nondeterminism. In this paper, we propose a simple extension to the standard situation calculus that accommodates nondeterministic actions and preserves Reiter’s solution to the frame problem and answering projection queries through regression. We also provide a formalization of FOND planning and show how ConGolog high-level program execution in nondeterministic domains can be defined.
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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".