{"id":"W2226121807","doi":"10.7939/r30w2k","title":"Using behaviour patterns to generate scripts for computer role-playing games","year":2009,"lang":"en","type":"article","venue":"University of Alberta Library","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Scripting language; Computer science; Code (set theory); Human–computer interaction; Reinforcement learning; Character (mathematics); Computer game; Artificial intelligence; Multimedia; Programming language","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":[0.001659575,0.0009100817,0.0003299787,0.0006787595,0.000372569,0.001143069,0.001623455,0.0007071883,0.002079467],"category_scores_gemma":[0.00870872,0.0007750641,0.0007057862,0.0002792256,0.001242472,0.00157208,0.001003689,0.00104393,0.0008782449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00118438,"about_ca_system_score_gemma":0.001013762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002367465,"about_ca_topic_score_gemma":0.004047518,"domain_scores_codex":[0.9984617,0.0006553971,0.0001635739,0.0002976955,0.0003322417,0.00008944401],"domain_scores_gemma":[0.9967062,0.001818768,0.0003494637,0.0005957921,0.0003866652,0.000143168],"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":[0.0005916752,0.0009205513,0.01855261,0.0009354068,0.0001504768,0.001160802,0.007632231,0.3127346,0.05124801,0.1451813,0.007319804,0.4535726],"study_design_scores_gemma":[0.00008210604,0.0001592853,0.001050012,0.00007990585,0.00004572671,0.0003070688,0.0002255083,0.9219977,0.0224453,0.03396269,0.01959192,0.00005281132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03164906,0.0000392396,0.9599025,0.0001259246,0.00002084699,0.0004407298,0.0001058964,0.00467376,0.003042142],"genre_scores_gemma":[0.2753805,0.00008564552,0.7191089,0.00008944343,0.000006635929,0.0005989785,0.0003608103,0.0009051891,0.003463816],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002367465,"threshold_uncertainty_score":0.008776784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03321201273581672,"score_gpt":0.2393984965559832,"score_spread":0.2061864838201665,"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."}}