{"id":"W2053870186","doi":"10.2316/journal.206.2006.2.206-2794","title":"A NOVEL HYBRID NAVIGATION SCHEME FOR RECONFIGURABLE MULTI-AGENT TEAMS","year":2006,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Control reconfiguration; Computer science; Robustness (evolution); Scheme (mathematics); Task (project management); Distributed computing; Imperfect; Human–computer interaction; Real-time computing; Embedded system; Engineering; Systems engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0003479673,0.0003689187,0.0003927183,0.0003377202,0.0004754324,0.0004378204,0.00128402,0.0006098627,0.001559961],"category_scores_gemma":[0.0007032764,0.0001980637,0.0003113312,0.000289451,0.0004009356,0.0007807256,0.001029181,0.0004610507,0.0003909787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002906237,"about_ca_system_score_gemma":0.0003140263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001076822,"about_ca_topic_score_gemma":0.0009952603,"domain_scores_codex":[0.9997032,0.00006943561,0.00002267161,0.00007364953,0.00009573044,0.00003533683],"domain_scores_gemma":[0.999645,0.00008200089,0.00005287412,0.0000979042,0.0000708903,0.00005128696],"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":[0.0003564659,0.0000908497,0.00123412,0.0001864072,0.0001059595,0.0002816405,0.0003494334,0.549606,0.06643628,0.04021396,0.002621484,0.3385175],"study_design_scores_gemma":[0.00005870412,0.0001657783,0.0002457874,0.0000107999,0.00001968841,0.0001496488,0.00002780607,0.9817207,0.006138168,0.004605044,0.006829777,0.00002822834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02625693,0.0001477296,0.97091,0.00006064522,0.00007937994,0.00004192447,0.00002308124,0.0005277389,0.001952639],"genre_scores_gemma":[0.568446,0.0001220301,0.4271545,0.00007835095,0.00004329922,0.00016719,0.00008637867,0.00005032819,0.003851906],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001559961,"threshold_uncertainty_score":0.005218625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01997880030245743,"score_gpt":0.2768774052905396,"score_spread":0.2568986049880821,"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."}}