{"id":"W2975442767","doi":"10.1109/cig.2019.8848095","title":"Deep Variational Autoencoders for NPC Behaviour Classification","year":2019,"lang":"en","type":"article","venue":"2019 IEEE Conference on Games (CoG)","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Computer science; Deep learning; Set (abstract data type); Machine learning; Unsupervised learning; Video game; Training set; Pattern recognition (psychology); Multimedia","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":[0.0004715979,0.0006317,0.000366978,0.0002326103,0.0001616572,0.000370115,0.0007127482,0.0005772724,0.001244587],"category_scores_gemma":[0.001483773,0.000311249,0.0004932755,0.0001838764,0.0004005487,0.0004191482,0.0004442169,0.001157784,0.0003579356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007514743,"about_ca_system_score_gemma":0.0005438752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008499448,"about_ca_topic_score_gemma":0.01088677,"domain_scores_codex":[0.9998319,0.00004989149,0.000007881982,0.00004784726,0.00003133306,0.00003115194],"domain_scores_gemma":[0.999616,0.0002140303,0.00003596754,0.00004214265,0.00006763365,0.00002405805],"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":[0.0001098403,0.0001212256,0.002682046,0.00005168352,0.00007064986,0.00005129838,0.00007902176,0.8705778,0.009604066,0.004678622,0.00148681,0.1104869],"study_design_scores_gemma":[0.000001341194,0.000006769123,0.0002158482,0.000002005563,0.000001700947,0.000002973327,0.000003055151,0.9985145,0.0006361487,0.0005093284,0.0001046881,0.000001662876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2289196,0.0007118169,0.7620419,0.0003271023,0.00009218525,0.00009213796,0.0003399552,0.001980468,0.005494916],"genre_scores_gemma":[0.895306,0.00015357,0.09793658,0.0001089938,0.00001795776,0.00007177606,0.0006311591,0.0001055407,0.005668432],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008499448,"threshold_uncertainty_score":0.01689994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06522760177606564,"score_gpt":0.3101919606265625,"score_spread":0.2449643588504968,"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."}}