{"id":"W2064948331","doi":"10.1109/icicisys.2009.5358028","title":"The adaptive learning mechanism design for game agents' real-time behavior control","year":2009,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Adaptability; Computer science; Key (lock); Mechanism (biology); Process (computing); Control (management); Adaptive behavior; Artificial intelligence; Adaptive learning; Game theory; Architecture","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":[0.00218846,0.0005999606,0.0005377078,0.0005884203,0.000478505,0.001267688,0.002142629,0.001265188,0.002314336],"category_scores_gemma":[0.00351995,0.0003398682,0.0008581255,0.0003450717,0.001547821,0.001441451,0.001100607,0.001520808,0.0003635071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001264372,"about_ca_system_score_gemma":0.001613952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00177987,"about_ca_topic_score_gemma":0.0009404093,"domain_scores_codex":[0.9987024,0.0003403658,0.0001023111,0.0003229078,0.0004153213,0.0001167332],"domain_scores_gemma":[0.9990185,0.0003244964,0.0001456876,0.0001346053,0.0002961962,0.00008052748],"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":[0.0001196022,0.0001934586,0.0009562903,0.0003171639,0.0001884171,0.0003178746,0.0005133532,0.3237052,0.01791137,0.5469902,0.001727864,0.1070593],"study_design_scores_gemma":[0.0000819899,0.0001577511,0.0002775743,0.0000319088,0.00006086699,0.0001361583,0.00003326561,0.8718863,0.005833471,0.113474,0.007990628,0.00003608486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004004066,0.0001165389,0.9927012,0.0001842287,0.00004239763,0.00007484829,0.000007258978,0.000149043,0.002720404],"genre_scores_gemma":[0.612123,0.0003212725,0.3808726,0.0002679235,0.00006753738,0.0007785019,0.00003413904,0.00004844868,0.0054866],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002314336,"threshold_uncertainty_score":0.01157379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0514844078886825,"score_gpt":0.2998908334576668,"score_spread":0.2484064255689843,"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."}}