{"id":"W2120406836","doi":"","title":"APRICODD: Approximate Policy Construction Using Decision Diagrams","year":2000,"lang":"en","type":"article","venue":"","topic":"Formal Methods in Verification","field":"Computer Science","cited_by":121,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of British Columbia","funders":"","keywords":"Markov decision process; Dynamic programming; Influence diagram; Computer science; Mathematical optimization; Value (mathematics); Bellman equation; Class (philosophy); Markov process; Space (punctuation); Algebraic number; Mathematics; Decision tree; Artificial intelligence; Machine learning; Statistics","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":[0.0003427852,0.00009678441,0.00009808277,0.0001339913,0.0001354467,0.0001516167,0.0004689442,0.00006229098,0.0001217851],"category_scores_gemma":[0.0000691469,0.00008445826,0.00003981949,0.0007321056,0.00005894544,0.0007929484,0.00007311728,0.0000754566,0.0001307285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000850658,"about_ca_system_score_gemma":0.00005132369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007921926,"about_ca_topic_score_gemma":9.628978e-7,"domain_scores_codex":[0.9989939,0.00006620202,0.0002206485,0.0002773838,0.0002218023,0.0002200792],"domain_scores_gemma":[0.9992547,0.00004321692,0.00005577175,0.0005324665,0.00004259014,0.00007127815],"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":[0.000003322479,0.00001352641,0.0001057468,0.000002125585,0.000001653257,4.797137e-7,0.00006586669,0.0003954207,0.0004890896,0.2212823,0.00001771498,0.7776228],"study_design_scores_gemma":[0.0002139439,0.00003272556,0.001544864,0.00001367078,0.000002897644,0.0001171588,0.0000157329,0.9340019,0.009721531,0.0507961,0.003345127,0.0001943749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09710269,0.00001352083,0.8894272,0.00007948111,0.0002237774,0.0001054673,3.778136e-7,0.0002262685,0.01282119],"genre_scores_gemma":[0.06013554,0.00002537761,0.9394351,0.0001565595,0.00008862793,0.000004433559,6.792343e-7,0.000006025342,0.0001477192],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9336064,"threshold_uncertainty_score":0.3444108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03039523163578827,"score_gpt":0.3178480168016136,"score_spread":0.2874527851658253,"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."}}