{"id":"W153946721","doi":"10.5555/2025816.2025820","title":"Implementing an efficient causal learning mechanism in a cognitive tutoring agent","year":2011,"lang":"en","type":"article","venue":"International Conference Industrial, Engineering & Other Applications Applied Intelligent Systems","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Mechanism (biology); Machine learning; Cognition; Constraint (computer-aided design); Bayesian network; Domain (mathematical analysis); Scalability; Causal structure; Psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001870716,0.0004691516,0.0005219814,0.0005489335,0.0007873995,0.001754121,0.002539292,0.001903291,0.005324912],"category_scores_gemma":[0.009755732,0.0004421342,0.0003693235,0.0002987882,0.0006066206,0.002120868,0.001572964,0.001294988,0.0008130566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004766122,"about_ca_system_score_gemma":0.001250139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001516349,"about_ca_topic_score_gemma":0.00153097,"domain_scores_codex":[0.9991666,0.0002684094,0.00007527043,0.0001861693,0.0002284309,0.00007509748],"domain_scores_gemma":[0.9957618,0.002181825,0.0002711054,0.0008085265,0.0006980481,0.0002786508],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001687047,0.002278816,0.01204635,0.0004939889,0.0003388608,0.001181365,0.001838096,0.1889463,0.1121792,0.1397366,0.006357678,0.5329157],"study_design_scores_gemma":[0.0001387696,0.0001619417,0.0004198438,0.00001276125,0.00008876958,0.0001344458,0.0000493146,0.9394072,0.03812441,0.01634703,0.005081203,0.0000343504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06307928,0.00004771916,0.9251132,0.0003230642,0.00009136657,0.0001766432,0.00004767267,0.007566115,0.003554988],"genre_scores_gemma":[0.6544439,0.00004019403,0.3424713,0.0001228051,0.00003746621,0.0001149832,0.00005644876,0.0001309298,0.002582033],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005324912,"threshold_uncertainty_score":0.01781356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1156566888426282,"score_gpt":0.2938824900319443,"score_spread":0.1782258011893161,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}