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Record W2190961789

How to progress beliefs in continuous domains

2014· article· en· W2190961789 on OpenAlex
Vaishak Belle, Hector J. Levesque

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEdinburgh Research Explorer (University of Edinburgh) · 2014
Typearticle
Languageen
FieldComputer Science
TopicLogic, Reasoning, and Knowledge
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProbabilistic logicRoboticsNoise (video)Artificial intelligenceComputer scienceAction (physics)Situation calculusHuman–computer interactionRobotCognitive sciencePsychology
DOInot available

Abstract

fetched live from OpenAlex

When Lin and Reiter introduced the progression of basic action theories in thesituation calculus, they were essentially motivated by long-lived roboticagents functioning over thousands of actions. However, their account does notdeal with probabilistic uncertainty about the initial situation nor witheffector or sensor noise, as often needed in robotic applications. In thispaper, we obtain results on how to progress continuous degrees of beliefagainst continuous effector and sensor noise in a semantically correctfashion. Most significantly, and perhaps surprisingly, we identify conditionsunder which our account is not only as efficient as the filtering mechanismscommonly used in robotics, but considerably more general.<br/>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0030.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.047
GPT teacher head0.284
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it