Bibliographic record
Abstract
Knowledge, Belief, and Noisy Sensing in the Situation Calculus Patricio D. Simari Master of Science Graduate Department of Computer Science University of Toronto 2004 The extension to the situation calculus presented by Bacchus et al. formalizes the concept of noisy actions and shows how an agent can update its beliefs, which are modeled probabilistically, when relying on noisy sensors and e#ectors. The extensions of Scherl and Levesque and Shapiro et al. also model knowledge and belief. While assuming noiseless actions and dealing with boolean beliefs, these frameworks support properties of knowledge and belief such as introspection about current and past beliefs. Here, it is shown how such properties of belief can be formalized and supported in the probabilistic Bacchus et al. extension. In addition, the concept of sensor coarseness is introduced and it is shown how it can be modeled in the Bacchus et al. framework. Finally, it is shown that the Bacchus et al. framework can function in a way which is equivalent to using conditional probability densities to combine noisy sensor readings.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".