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Record W131525165

Dual lookups in pattern databases

2005· article· en· W131525165 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

VenueInternational Joint Conference on Artificial Intelligence · 2005
Typearticle
Languageen
FieldComputer Science
TopicAI-based Problem Solving and Planning
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceLookup tableProtein Data Bank (RCSB PDB)HeuristicTable (database)Cube (algebra)Extension (predicate logic)Dual (grammatical number)Tree (set theory)DatabaseCombinatoricsProgramming languageMathematicsArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

A pattern database (PDB) is a heuristic function stored as a lookup table. Symmetries of a state space are often used to enable multiple values to be looked up in a PDB for a given state. This paper introduces an additional PDB lookup, called the dual PDB lookup. A dual PDB lookup is always admissible but can return inconsistent values. The paper also presents an extension of the well-known pathmax method so that inconsistencies in heuristic values are propagated in both directions (child-to-parent, and parent-to-child) in the search tree. Experiments show that the addition of dual lookups and bidirectional pathmax propagation can reduce the number of nodes generated by IDA* by over one order of magnitude in the TopSpin puzzle and Rubik's Cube, and by about a factor of two for the sliding tile puzzles.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.963
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.155
GPT teacher head0.336
Teacher spread0.182 · 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