{"id":"W2794543545","doi":"10.1609/aimag.v39i1.2777","title":"The First MicroRTS Artificial Intelligence Competition","year":2018,"lang":"en","type":"article","venue":"AI Magazine","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Competition (biology); Observability; Computer science; Artificial intelligence; Term (time); Operations research; Computational intelligence; Engineering; Mathematics","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":[0.009256239,0.001655438,0.001646369,0.002077733,0.003479697,0.01199751,0.002514344,0.003566958,0.07449325],"category_scores_gemma":[0.0177547,0.0005311116,0.001555761,0.001325921,0.002123862,0.004542618,0.00558679,0.005550054,0.02543797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007152487,"about_ca_system_score_gemma":0.0082713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01207736,"about_ca_topic_score_gemma":0.02186019,"domain_scores_codex":[0.9910914,0.001786018,0.0002381012,0.0007698655,0.004852391,0.001262174],"domain_scores_gemma":[0.9877276,0.001917689,0.0002186995,0.0007534035,0.004617236,0.004765416],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004584423,0.0001618437,0.0002953373,0.0001482376,0.00006127079,0.000154921,0.0001667573,0.002174303,0.0007922117,0.05001551,0.8954316,0.0501395],"study_design_scores_gemma":[0.00008642306,0.000149225,0.0008586103,0.00008320019,0.00001437194,0.00005425616,0.0001362644,0.002933904,0.0006298129,0.01581945,0.9791999,0.00003451623],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02871099,0.01195666,0.02633226,0.1142175,0.09587154,0.0005892253,0.006713482,0.002650762,0.7129576],"genre_scores_gemma":[0.1560384,0.004154151,0.01642667,0.01506168,0.01351216,0.0006976094,0.01118775,0.002317292,0.7806042],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07449325,"threshold_uncertainty_score":0.2492048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02716595881467287,"score_gpt":0.2869183879692783,"score_spread":0.2597524291546054,"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."}}