Projection of Belief in the Presence of Nondeterministic Actions and Fallible Sensing
Bibliographic record
Abstract
In a recent paper, we presented a Situation Calculus-based framework for modelling an agent that has incomplete or inaccurate knowledge about its environments, whose actions are non-deterministic, and whose sensor might give incorrect results. Generalizing earlier proposals, the presented approach represented the agent's epistemic state by a set of situations ranked by their respective plausibility, which would then be updated by modifying the plausibility ranks accordingly. In this short paper, we extend our earlier work by considering the problem of projection in this framework, i.e. the question whether a certain (epistemic) formula will hold after a given sequence of actions. We present results on both regression, where the query is transformed into an equivalent one about the initial situation, as well as progression, where the knowledge base is updated to reflect the situation after executing the action sequence in question.
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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".