{"id":"W4285134718","doi":"10.1109/tai.2022.3178065","title":"Toward Personalization of User Preferences in Partially Observable Smart Home Environments","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Personalization; Reinforcement learning; Computer science; Preference; Home automation; Baseline (sea); Human–computer interaction; Machine learning; Artificial intelligence; World Wide Web; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001866593,0.0005866094,0.0008005656,0.0003523326,0.0002048301,0.0005380207,0.0006589756,0.0005365711,0.0005614201],"category_scores_gemma":[0.006741515,0.0003757815,0.0004331981,0.0002356244,0.000473671,0.001406952,0.0007213022,0.001029633,0.0001593636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006366834,"about_ca_system_score_gemma":0.0006534038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00589569,"about_ca_topic_score_gemma":0.006797519,"domain_scores_codex":[0.9990689,0.0004214403,0.00003668351,0.0002221917,0.0001659783,0.00008473344],"domain_scores_gemma":[0.9976299,0.001409712,0.0002673551,0.0003006904,0.0002763105,0.0001160398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005028967,0.0003679204,0.02265294,0.0001031983,0.0001347141,0.000130755,0.0007321111,0.7979857,0.007008381,0.008256242,0.0009202639,0.1612049],"study_design_scores_gemma":[0.000008794843,0.00005080169,0.001187885,0.000003438373,0.000006897939,0.00001540079,0.00002457226,0.9945058,0.0007223724,0.003310269,0.0001576689,0.000006184441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1992818,0.000261416,0.7985743,0.0002021402,0.00001603327,0.00005902434,0.00007690791,0.000527518,0.001000864],"genre_scores_gemma":[0.9544015,0.00008705234,0.04461822,0.0000599014,0.00001124174,0.0000376092,0.00007896314,0.00002120458,0.000684255],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00589569,"threshold_uncertainty_score":0.0117228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1171837084210457,"score_gpt":0.2788703902975181,"score_spread":0.1616866818764723,"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."}}