{"id":"W2093240210","doi":"10.1109/wac.2014.6936067","title":"An improved PSO-based approach with dynamic parameter tuning for cooperative target searching of multi-robots","year":2014,"lang":"en","type":"article","venue":"","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Robot; Particle swarm optimization; Computer science; Mobile robot; Field (mathematics); Fitness function; Function (biology); Swarm behaviour; Mathematical optimization; Swarm robotics; Potential field; Artificial intelligence; Machine learning; Mathematics; Genetic algorithm","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":[0.0004758528,0.0007153473,0.0007854533,0.0004940811,0.0003466427,0.0005072013,0.001001467,0.000806233,0.0009552563],"category_scores_gemma":[0.0009585744,0.0003383752,0.0006611493,0.0004255692,0.0003797149,0.0007736676,0.0006717808,0.0007076626,0.0002681127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002893272,"about_ca_system_score_gemma":0.0005966298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002084278,"about_ca_topic_score_gemma":0.00156378,"domain_scores_codex":[0.9996955,0.00007611448,0.00002067419,0.00006253428,0.0001164826,0.00002874658],"domain_scores_gemma":[0.9997875,0.00006663666,0.00003028201,0.00002860044,0.00006961531,0.00001734607],"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":[0.00005519835,0.00006308703,0.000780976,0.0001140609,0.00006562153,0.0001215544,0.00009169878,0.9071695,0.0102139,0.006115923,0.0008339063,0.07437465],"study_design_scores_gemma":[0.0000127629,0.00004190138,0.0001549323,0.000003553529,0.000008356851,0.00003040003,0.000007037752,0.9976452,0.0005784861,0.000826104,0.000685183,0.000006052295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01343922,0.0003112493,0.9827062,0.00008674203,0.00006069694,0.00004316278,0.00001042857,0.0002338259,0.003108439],"genre_scores_gemma":[0.7366168,0.0003792188,0.2592012,0.0001289827,0.00007085811,0.0002555254,0.00007314421,0.00006811648,0.003206176],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002084278,"threshold_uncertainty_score":0.004144251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02056997719486533,"score_gpt":0.2726823220681812,"score_spread":0.2521123448733159,"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."}}