{"id":"W4391697002","doi":"10.1109/tai.2024.3364127","title":"Alternating Direction Method of Multipliers-Based Parallel Optimization for Multi-Agent Collision-Free Model Predictive Control","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Mathematical optimization; Initialization; Quadratic programming; Computer science; Convergence (economics); Convex optimization; Model predictive control; Integer programming; Quadratically constrained quadratic program; Quadratic equation; Optimization problem; Regular polygon; Mathematics; Control (management); Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003234215,0.0002478525,0.0002895878,0.0003161298,0.0001512209,0.0000594206,0.000184118,0.0001453147,0.00001558481],"category_scores_gemma":[0.00006246731,0.0002647535,0.0001914129,0.0003802505,0.00004229705,0.0002845514,0.000001003947,0.000203719,0.000006697904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002579874,"about_ca_system_score_gemma":0.00005106213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002380396,"about_ca_topic_score_gemma":0.0000458029,"domain_scores_codex":[0.9983788,0.00006271699,0.0006799102,0.0003767149,0.0002498573,0.0002519957],"domain_scores_gemma":[0.9988188,0.0004872992,0.00008930267,0.0002946515,0.0002321321,0.00007784674],"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":[0.0002186281,0.00009392844,2.411741e-7,0.0001294525,0.00009352416,7.962791e-7,0.0002730555,0.9520357,0.00721461,0.0002964636,0.000007076778,0.03963652],"study_design_scores_gemma":[0.0002610169,0.0001066075,2.645429e-7,0.0001309192,0.00009532755,0.000001145278,0.00009181837,0.8849055,0.1137826,0.0004220633,0.000008386086,0.000194381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001202117,0.0001127436,0.995885,0.00005008102,0.001470608,0.001347354,0.000363663,0.0006086039,0.00004176225],"genre_scores_gemma":[0.5886737,0.0000251595,0.4107126,0.00001254545,0.0000449521,0.000440267,0.000007257504,0.00005256319,0.00003093932],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5885535,"threshold_uncertainty_score":0.9999804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04486114021600532,"score_gpt":0.3181066622173674,"score_spread":0.2732455220013621,"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."}}