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Record W1800916125

Symbolic dynamic programming for first-order MDPs

2001· article· en· W1800916125 on OpenAlex

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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicBayesian Modeling and Causal Inference
Canadian institutionsUniversity of British ColumbiaUniversity of Toronto
Fundersnot available
KeywordsMarkov decision processBellman equationDynamic programmingEnumerationState spaceComputer scienceMathematical optimizationState (computer science)Set (abstract data type)Partition (number theory)Stochastic programmingFunction (biology)Value (mathematics)Markov processMathematicsAlgorithmDiscrete mathematicsProgramming language
DOInot available

Abstract

fetched live from OpenAlex

We present a dynamic programming approach for the solution of first-order Markov decisions processes. This technique uses an MDP whose dynamics is represented in a variant of the situation calculus allowing for stochastic actions. It produces a logical description of the optimal value function and policy by constructing a set of first-order formulae that minimally partition state space according to distinctions made by the value function and policy. This is achieved through the use of an operation known as decision-theoretic regression. In effect, our algorithm performs value iteration without explicit enumeration of either the state or action spaces of the MDP. This allows problems involving relational fluents and quantification to be solved without requiring explicit state space enumeration or conversion to propositional form. 1

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: Methods · Consensus signal: none
Teacher disagreement score0.955
Threshold uncertainty score0.379

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 science0.0010.000
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 head0.022
GPT teacher head0.278
Teacher spread0.255 · 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

Quick stats

Citations216
Published2001
Admission routes1
Has abstractyes

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