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Record W79747542 · doi:10.5555/1838206.1838398

Quasi deterministic POMDPs and DecPOMDPs

2010· article· en· W79747542 on OpenAlex
Camille Besse, Brahim Chaib-draa

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

VenueAdaptive Agents and Multi-Agents Systems · 2010
Typearticle
Languageen
FieldComputer Science
TopicBayesian Modeling and Causal Inference
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsObservabilityObservableComputer scienceExtension (predicate logic)Markov decision processMathematical optimizationHierarchyClass (philosophy)Polynomial hierarchyTheoretical computer scienceMathematicsTime complexityMarkov processApplied mathematicsAlgorithmArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, we study a particular subclass of partially observable models, called quasi-deterministic partially observable Markov decision processes (qDET-POMDPs), characterized by deterministic transitions and stochastic observations. While this framework does not model the same general problems as POMDPs, it still captures a number of interesting and challenging problems and have, in some cases, interesting properties. By studying the observability available in this subclass, we suggest that qDET-POMDPs may fall many steps in the complexity hierarchy. An extension of this framework to the decentralized case also reveals a subclass of numerous problems that can be approximated in polynomial space.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.971
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.061
GPT teacher head0.294
Teacher spread0.234 · 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