{"id":"W2098487995","doi":"10.1609/aiide.v7i1.12435","title":"Build Order Optimization in StarCraft","year":2011,"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":101,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Heuristics; Computer science; Action (physics); Order (exchange); Unit (ring theory); Resource (disambiguation); Resource allocation; Pathfinding; Mathematical optimization; Artificial intelligence; Theoretical computer science; Shortest path problem; 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":[0.0006044167,0.0008193463,0.0009912541,0.0005686149,0.0005088794,0.0009687504,0.0007471734,0.0008637102,0.006465379],"category_scores_gemma":[0.002274499,0.000571164,0.0006783634,0.0006192845,0.001658241,0.001424802,0.001401283,0.001546541,0.0004677793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001160671,"about_ca_system_score_gemma":0.001121769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006292224,"about_ca_topic_score_gemma":0.006944578,"domain_scores_codex":[0.9995325,0.0001527966,0.00002044054,0.0001060619,0.0001177795,0.00007042292],"domain_scores_gemma":[0.9991807,0.0005658638,0.00006782564,0.00005082129,0.00006115293,0.00007366673],"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":[0.000133518,0.00007913408,0.0006950606,0.0001576193,0.00002975069,0.0001124445,0.0001790559,0.8285033,0.001182346,0.1387358,0.00211325,0.02807868],"study_design_scores_gemma":[0.000052558,0.00008300984,0.000189131,0.0000241257,0.00001166843,0.00003611849,0.00008074214,0.8523071,0.0006705655,0.1411347,0.00539843,0.00001198882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09696374,0.00123657,0.8749358,0.0005334149,0.000101981,0.0001242834,0.0001967479,0.0005430928,0.02536432],"genre_scores_gemma":[0.6833302,0.0008783545,0.299269,0.0001881506,0.00005236071,0.0002121659,0.0003615935,0.0002678856,0.01544022],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006465379,"threshold_uncertainty_score":0.02162886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0564658959255539,"score_gpt":0.2790584538778487,"score_spread":0.2225925579522948,"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."}}