{"id":"W4385301065","doi":"10.1109/gas59301.2023.00011","title":"Assessing Video Game Balance using Autonomous Agents","year":2023,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; École de Technologie Supérieure","funders":"","keywords":"Game design; Computer science; Video game design; Game testing; Game Developer; Video game; Game mechanics; Game design document; Game art design; Game development tool; Video game development; Multimedia; Human–computer interaction; Level design; Scope (computer science); Balance (ability); 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":[0.002777996,0.0008876235,0.0004988674,0.001992263,0.0004236615,0.001796187,0.000834788,0.0006964331,0.001392986],"category_scores_gemma":[0.01742287,0.0003371048,0.0002862105,0.0005185992,0.0004755287,0.001698026,0.00125352,0.000453445,0.0004455746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006786841,"about_ca_system_score_gemma":0.0004396151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003077828,"about_ca_topic_score_gemma":0.003349986,"domain_scores_codex":[0.9971179,0.001072038,0.000255606,0.0004016055,0.0009845262,0.0001683658],"domain_scores_gemma":[0.9909861,0.004116619,0.001391115,0.0005005962,0.002188343,0.0008172017],"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":[0.003960103,0.005296906,0.3720885,0.0007598521,0.0006275063,0.0005662186,0.007313018,0.04533716,0.1335233,0.00468471,0.0023761,0.4234665],"study_design_scores_gemma":[0.0005434944,0.01493131,0.4375321,0.0002292967,0.0004129032,0.0007707721,0.005220333,0.4577156,0.06543849,0.007157646,0.009775918,0.0002721942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9737498,0.00006749342,0.02208216,0.00004439736,0.0000152428,0.0003471361,0.00007517259,0.0002324974,0.003386124],"genre_scores_gemma":[0.9755601,0.00004884398,0.02296311,0.00002763298,0.000006166145,0.0002118954,0.0001775994,0.00002826172,0.0009763761],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003077828,"threshold_uncertainty_score":0.01469159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1351621642895066,"score_gpt":0.3804072405536696,"score_spread":0.2452450762641631,"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."}}