{"id":"W26292495","doi":"","title":"Decision Making under Uncertainty: Operations Research Meets AI (Again)","year":2000,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Exploit; Artificial intelligence; Machine learning; Probabilistic logic; Markov decision process; Representation (politics); Partially observable Markov decision process; Bayesian network; Automated planning and scheduling; Influence diagram; Markov process; Markov chain; Markov model; Decision tree; 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":[0.007112472,0.001183567,0.001446088,0.001883378,0.001886997,0.01436485,0.001014435,0.006529858,0.01123562],"category_scores_gemma":[0.01769971,0.0005141463,0.0006550982,0.005210999,0.01041636,0.01933828,0.003867853,0.007248634,0.002821384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004668496,"about_ca_system_score_gemma":0.00522043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004069678,"about_ca_topic_score_gemma":0.002890259,"domain_scores_codex":[0.9932002,0.003341005,0.0003887612,0.0006320073,0.002053668,0.0003843808],"domain_scores_gemma":[0.9926451,0.004442157,0.0006768395,0.0005562461,0.001117602,0.0005619109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001832764,0.00001700262,0.0001307158,0.0002087044,0.00002354735,0.0000561013,0.0002766424,0.002562196,0.00008177269,0.9118838,0.03708412,0.04765717],"study_design_scores_gemma":[0.000005228185,0.00001146009,0.00008167336,0.0002232422,0.000004366795,0.00003966614,0.0002126648,0.002829583,0.00003354142,0.9350594,0.06148803,0.00001110536],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"methods","genre_scores_codex":[0.003432631,0.2062027,0.1749965,0.4405901,0.01021077,0.00008485664,0.000269377,0.0002617087,0.1639514],"genre_scores_gemma":[0.4393242,0.2760221,0.1543549,0.05624207,0.04081613,0.000703161,0.0003938561,0.0002759694,0.03186765],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01436485,"threshold_uncertainty_score":0.03761482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2736602822281722,"score_gpt":0.4565537726636735,"score_spread":0.1828934904355012,"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."}}