{"id":"W2887000122","doi":"10.1109/civemsa.2018.8439974","title":"Model-Free Value Iteration Solution for Dynamic Graphical Games","year":2018,"lang":"en","type":"article","venue":"","topic":"Adaptive Dynamic Programming Control","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Perceptron; Reinforcement learning; A priori and a posteriori; Graph; Graphical model; Set (abstract data type); Artificial neural network; Artificial intelligence; Theoretical computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003022477,0.0001207351,0.0001158041,0.0001035472,0.0001739655,0.0001480038,0.0007747097,0.00007605111,0.00000194203],"category_scores_gemma":[0.0000961545,0.0001066148,0.00008832723,0.0001926663,0.0001042147,0.0005317253,0.0001974539,0.00006240857,0.00001576106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007736654,"about_ca_system_score_gemma":0.00006151139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000127083,"about_ca_topic_score_gemma":0.00009977996,"domain_scores_codex":[0.9988952,0.00003421179,0.0001823992,0.0003890761,0.0001944672,0.0003046702],"domain_scores_gemma":[0.9989818,0.00006795234,0.00005915101,0.0006198463,0.0002133846,0.00005789757],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001428295,0.00002855487,0.00001649617,0.000003680981,0.00001283005,3.386595e-7,0.0001245816,0.0002314129,0.002011249,0.9465353,0.000730034,0.05029122],"study_design_scores_gemma":[0.0004222809,0.0001716495,0.0001152414,0.000004394306,0.000005633969,0.000002894533,0.000001897831,0.8009946,0.000111228,0.1976679,0.0003865892,0.0001157447],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002041511,0.00001984318,0.9919252,0.004576646,0.0002521007,0.0003779851,0.000004095275,0.0003425895,0.0004600275],"genre_scores_gemma":[0.5601113,6.656711e-7,0.4390604,0.0003947983,0.00004697085,0.00004785407,0.000004251645,0.000005822635,0.0003278886],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8007632,"threshold_uncertainty_score":0.4347627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01436888469776386,"score_gpt":0.2655323166783256,"score_spread":0.2511634319805617,"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."}}