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Record W2160149488 · doi:10.1109/icnn.1996.549071

Neurosolver solves blocks world problems

2002· article· en· W2160149488 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

VenueProceedings of International Conference on Neural Networks (ICNN'96) · 2002
Typearticle
Languageen
FieldComputer Science
TopicEvolutionary Algorithms and Applications
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceConstruct (python library)Problem solverTask (project management)SolverProcess (computing)Artificial intelligenceNeuromorphic engineeringControl (management)Space (punctuation)State (computer science)Theoretical computer scienceProgramming languageSoftware engineeringArtificial neural networkEngineering

Abstract

fetched live from OpenAlex

We report an ongoing work on a biologically inspired device, the Neurosolver, introduced in our (1995) earlier paper. To explore and improve the Neurosolver's capabilities, we attempt to apply it to a task of rearranging three different blocks in a blocks world similar to the worlds known from the classic AI literature. The Neurosolver records the observed trajectories in the state space of the blocks world and uses the learned traces to perform searches and construct plans to control the movements of the blocks. This work is a part of a broader effort to devise neurally-based agents that would cooperate en masse in a process of solving complex problems. We consider it a next step toward a Neuromorphic General Problem Solver.

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 categoriesMeta-epidemiology (narrow)
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 score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.042
GPT teacher head0.250
Teacher spread0.208 · 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