{"id":"W4402811239","doi":"10.1109/tie.2024.3451055","title":"Global Temporal Logic Control Synthesis for Multiagent Systems With Time and Space Margin","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Temporal logic; Computer science; Margin (machine learning); Multi-agent system; Control (management); Control system; Control engineering; Control theory (sociology); Artificial intelligence; Engineering; Theoretical computer science; Machine learning; Electrical engineering","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":[0.0007556716,0.001035385,0.0005651305,0.0003892464,0.0004931958,0.001413036,0.0007780441,0.0005213783,0.002228284],"category_scores_gemma":[0.001222076,0.000271405,0.0007197051,0.0004570891,0.0008167218,0.001004713,0.001197616,0.0009849693,0.0002633764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009199629,"about_ca_system_score_gemma":0.001268366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003453995,"about_ca_topic_score_gemma":0.003283615,"domain_scores_codex":[0.9995079,0.00009322756,0.00002751072,0.0001264195,0.0001714246,0.00007349301],"domain_scores_gemma":[0.999572,0.0001850053,0.0000943931,0.00004099606,0.00007841158,0.0000292487],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004594268,0.00001954641,0.0001798315,0.0001053185,0.00001794513,0.0001179663,0.00008633845,0.9292654,0.004813779,0.03720877,0.0004342461,0.02770495],"study_design_scores_gemma":[0.000007056538,0.00003725255,0.0000392473,0.000007481554,0.000006645658,0.00001495828,0.00001344464,0.988118,0.00102837,0.009776021,0.0009477932,0.000003729171],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006482482,0.0001417966,0.9892972,0.00007543981,0.0000214451,0.00002988006,0.00002499034,0.0001279495,0.003798852],"genre_scores_gemma":[0.8161908,0.000344589,0.179648,0.00009151962,0.00004944379,0.00019375,0.000123865,0.00006629865,0.003291701],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003453995,"threshold_uncertainty_score":0.007454395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02125511119863892,"score_gpt":0.2382611981330653,"score_spread":0.2170060869344263,"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."}}