{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003407078,0.000121203,0.0001331936,0.0001437792,0.0001387508,0.0006426329,0.0008542867,0.00005249617,0.00008819832],"category_scores_gemma":[0.0001003582,0.0001151,0.00005634128,0.0009158805,0.0000531053,0.001369791,0.0004101704,0.000100599,0.00139581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007959682,"about_ca_system_score_gemma":0.00009610539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000104319,"about_ca_topic_score_gemma":0.000006804414,"domain_scores_codex":[0.9985994,0.0000484361,0.0002588991,0.0004024584,0.0002748971,0.0004159882],"domain_scores_gemma":[0.9991297,0.0001288915,0.00007446355,0.0005180672,0.00006164205,0.00008722654],"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":[0.000004871605,0.0001730787,0.02767911,0.00005670148,0.00007442253,0.0006116249,0.006482335,0.07708011,0.05475288,0.1267839,0.01514347,0.6911575],"study_design_scores_gemma":[0.00002180911,0.00000912006,0.003425635,0.00001993655,0.000001909349,0.000009897113,0.0001225952,0.9729905,0.01378178,0.006535785,0.002919174,0.0001619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2796544,0.00001689266,0.7121137,0.000554539,0.0008049376,0.00008066803,3.197149e-7,0.0009292687,0.005845274],"genre_scores_gemma":[0.9494697,0.000005508882,0.04863308,0.0004901997,0.00009245343,0.000004615496,7.482153e-7,0.00001210915,0.001291543],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8959103,"threshold_uncertainty_score":0.9993817,"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."}}