{"id":"W2786374877","doi":"10.1109/iscmi.2017.8279623","title":"Analysis and comparison of two task models in a partially observable Markov decision process based assistive system","year":2017,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Task (project management); Computer science; Process (computing); Partially observable Markov decision process; Human–computer interaction; Activities of daily living; Task analysis; Field (mathematics); Dementia; Independence (probability theory); Cognition; Assistive technology; Artificial intelligence; Markov model; Psychology; Markov chain; Machine learning; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008653157,0.0001593317,0.0006706248,0.0003295635,0.0001861236,0.0004018301,0.0008381564,0.00006772891,0.000006583942],"category_scores_gemma":[0.0001083883,0.0001408989,0.0001034486,0.0005190042,0.00005490491,0.001286563,0.0002352171,0.00009678957,0.000004644384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007029432,"about_ca_system_score_gemma":0.0001292917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001896056,"about_ca_topic_score_gemma":0.009001469,"domain_scores_codex":[0.9980748,0.0001618921,0.0005520626,0.0005396435,0.000451936,0.000219646],"domain_scores_gemma":[0.9976735,0.0004138516,0.0005356513,0.0009446112,0.0003289941,0.0001033663],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008673412,0.0002659402,0.9069288,0.0001726483,0.0001945846,0.00001569221,0.0006512514,0.01007479,0.0002789072,0.0008976054,0.00003727631,0.08039571],"study_design_scores_gemma":[0.000768929,0.00003712243,0.1184441,0.0001806995,0.00005915824,0.000001597196,0.0001382544,0.8786399,0.001386307,0.0001950303,0.00000527948,0.0001435439],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.321945,0.00002693499,0.6761562,0.00006681358,0.00006144492,0.0002131147,0.000007128929,0.00004738514,0.001476024],"genre_scores_gemma":[0.9877232,6.636823e-7,0.01215054,0.00001941509,0.00001014077,0.00004660616,0.000002405532,0.000006070441,0.00004098346],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8685651,"threshold_uncertainty_score":0.574569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06425904136245943,"score_gpt":0.3381007410648859,"score_spread":0.2738416997024265,"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."}}