MétaCan
Menu
Back to cohort
Record W2078022838 · doi:10.1080/08839510701492579

A KEYHOLE PLAN RECOGNITION MODEL FOR ALZHEIMER'S PATIENTS: FIRST RESULTS

2007· article· en· W2078022838 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

VenueApplied Artificial Intelligence · 2007
Typearticle
Languageen
FieldComputer Science
TopicAI-based Problem Solving and Planning
Canadian institutionsUniversité du Québec à ChicoutimiUniversité de Sherbrooke
Fundersnot available
KeywordsComputer sciencePlan (archaeology)KeyholeArtificial intelligenceCognitive impairmentSet (abstract data type)CognitionCognitive modelMachine learningCognitive psychologyPsychologyProgramming languagePsychiatry

Abstract

fetched live from OpenAlex

This article addresses the problem of recognizing the behavior of a person suffering from Alzheimer's disease at early-intermediate stages. We present a keyhole plan recognition model, based on lattice theory and action description logic, which transforms the recognition problem into a classification issue. This approach allows us to formalize the plausible incoherent intentions of the patient, resulting from the symptoms of his cognitive impairment, such as disorientation, memory lapse, etc. An implementation of this model was tested in our smart home laboratory, by simulating a set of real case scenarios.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.883
Threshold uncertainty score0.828

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 communication0.0000.000
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 head0.118
GPT teacher head0.288
Teacher spread0.169 · 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