{"id":"W1988196484","doi":"10.1109/tnn.2011.2169808","title":"Bioinspired Neural Network for Real-Time Cooperative Hunting by Multirobots in Unknown Environments","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":126,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Robot; Computer science; Artificial neural network; Artificial intelligence; Robotics; Motion planning; Alliance; Path (computing); Collision avoidance; Order (exchange); Collision; Computer security","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":[0.0007072028,0.0004807555,0.0003967467,0.0002617777,0.0003260256,0.0004628307,0.0009978913,0.001020677,0.0009419812],"category_scores_gemma":[0.001470249,0.0002370542,0.0003400296,0.0002795969,0.0004645541,0.0008002479,0.0005704968,0.0008830483,0.0001568106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005907741,"about_ca_system_score_gemma":0.0006269654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004179981,"about_ca_topic_score_gemma":0.003211535,"domain_scores_codex":[0.9998053,0.00005078085,0.00001337509,0.00005242969,0.00005685157,0.00002135291],"domain_scores_gemma":[0.9996483,0.0001537463,0.00005167523,0.00002685108,0.000100174,0.00001912611],"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":[0.00006869181,0.00006413352,0.0005618009,0.00005429483,0.00003803915,0.00009996504,0.00006466248,0.9220831,0.004101175,0.003859837,0.0005958206,0.06840844],"study_design_scores_gemma":[0.000003630164,0.00001475564,0.00004941311,0.000002173839,0.000003041736,0.00000779628,0.000002400268,0.9987524,0.0003223178,0.0007069915,0.0001329432,0.000002281094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05579216,0.0009489135,0.9392302,0.0003250031,0.0001205181,0.00003864832,0.00002415913,0.0004856021,0.003034705],"genre_scores_gemma":[0.858046,0.0005368699,0.1372651,0.0001853379,0.00004073388,0.0001840598,0.0000895435,0.00002762545,0.003624643],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004179981,"threshold_uncertainty_score":0.008311331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02645550093874028,"score_gpt":0.238045988728049,"score_spread":0.2115904877893087,"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."}}