{"id":"W4312381398","doi":"10.1609/aiide.v11i2.12811","title":"StarCraft Unit Motion: Analysis and Search Enhancements","year":2015,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Motion (physics); Focus (optics); Scripting language; Set (abstract data type); Adversary; Unit (ring theory); Artificial intelligence; Motion planning; Robot; Programming language; Computer security; 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":[0.0003847675,0.00032876,0.0003868068,0.0007527306,0.0002241533,0.000680841,0.0007086743,0.0003341935,0.003627589],"category_scores_gemma":[0.004236087,0.0002456227,0.0004334383,0.0008683759,0.0003575341,0.001429053,0.0005466697,0.0005330134,0.0003396651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000562671,"about_ca_system_score_gemma":0.000476014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0061195,"about_ca_topic_score_gemma":0.004329321,"domain_scores_codex":[0.9997481,0.00006001523,0.00001296902,0.00005171579,0.00009473586,0.00003245885],"domain_scores_gemma":[0.9987526,0.0006797811,0.0001106277,0.0001979364,0.0002069173,0.00005214563],"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":[0.0003394648,0.000119183,0.00793737,0.0002226564,0.00004428394,0.0001490102,0.0001993261,0.7070889,0.01323064,0.07206699,0.002426403,0.1961757],"study_design_scores_gemma":[0.000006392374,0.00002996138,0.001076939,0.000006302603,0.000007035448,0.00004031029,0.00002180111,0.9925687,0.001667412,0.003697332,0.0008727267,0.000005052397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3796233,0.001268286,0.5973633,0.0004624276,0.00007484657,0.00009617338,0.0004073226,0.0008767082,0.01982768],"genre_scores_gemma":[0.9374685,0.0002836923,0.05884866,0.00003801531,0.00002087538,0.00003114572,0.0002517604,0.0001223381,0.002934994],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0061195,"threshold_uncertainty_score":0.01216775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09385145836623339,"score_gpt":0.3249705460475635,"score_spread":0.2311190876813301,"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."}}