{"id":"W2157261706","doi":"10.1109/tpami.2007.1145","title":"Value-Directed Human Behavior Analysis from Video Using Partially Observable Markov Decision Processes","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Partially observable Markov decision process; Computer science; Artificial intelligence; Markov decision process; Machine learning; Context (archaeology); Maximization; Markov process; Markov chain; Markov model; Mathematical optimization; Mathematics","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":[0.00146797,0.0008430845,0.0009117351,0.001346287,0.0003845305,0.0009229037,0.001109796,0.0006796518,0.001271864],"category_scores_gemma":[0.005906784,0.0005639956,0.0009370101,0.0008176881,0.0008541519,0.001412814,0.0009341305,0.00117682,0.0001809579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001431793,"about_ca_system_score_gemma":0.001103229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0131352,"about_ca_topic_score_gemma":0.01054145,"domain_scores_codex":[0.9990638,0.0003853976,0.00004026946,0.0002080409,0.0002408989,0.00006160978],"domain_scores_gemma":[0.9973257,0.002026872,0.0002652705,0.0001309727,0.0001718325,0.0000794308],"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":[0.0001665207,0.0001167848,0.003305166,0.0001280209,0.0001041513,0.0001619999,0.0002103385,0.8872842,0.0024438,0.01979451,0.0008474755,0.08543702],"study_design_scores_gemma":[0.000004787434,0.00001050293,0.0002134089,0.0000044096,0.000003341058,0.000007688925,0.000008710674,0.9901021,0.0004339528,0.00906986,0.0001366191,0.00000469549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01427855,0.000106498,0.9845194,0.0001148376,0.00000767545,0.0000579718,0.0001317686,0.0003354439,0.0004478458],"genre_scores_gemma":[0.6378011,0.0002570773,0.3601949,0.00006443622,0.00002911112,0.0002681498,0.0005940949,0.00006999243,0.0007212658],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0131352,"threshold_uncertainty_score":0.0261175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0319513845255916,"score_gpt":0.3118484687945718,"score_spread":0.2798970842689802,"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."}}