{"id":"W2152948384","doi":"10.1109/tro.2008.2007459","title":"A Distributed Heuristic for Energy-Efficient Multirobot Multiplace Rendezvous","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Rendezvous; Heuristic; Mobile robot; Robot; Computer science; Set (abstract data type); Bounded function; Mathematical optimization; Population; Controller (irrigation); Quality of service; Simple (philosophy); Distributed computing; Artificial intelligence; Mathematics; Engineering; Computer network","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":[],"consensus_categories":[],"category_scores_codex":[0.0001215315,0.0002039929,0.0002015027,0.0001685744,0.0005994958,0.00007050961,0.0004777988,0.0001008006,0.00001236515],"category_scores_gemma":[0.00002196162,0.000201128,0.0001503628,0.0005121465,0.0000892055,0.0001225997,0.000004284106,0.000187103,0.00003070812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001079569,"about_ca_system_score_gemma":0.0001143541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002106987,"about_ca_topic_score_gemma":0.00001607457,"domain_scores_codex":[0.9984115,0.00007204467,0.0003139739,0.0004404618,0.0003374357,0.0004246008],"domain_scores_gemma":[0.9986637,0.0003063755,0.00007481076,0.0005324529,0.0002148239,0.0002078614],"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":[0.0000224989,0.0004663569,0.00000148481,0.00001069928,0.00002123511,0.00001099478,0.0002593381,0.9937377,0.00009312422,0.002129327,0.000603053,0.002644217],"study_design_scores_gemma":[0.001033097,0.0001766676,0.0000161884,0.00001504066,0.00001234557,0.00003743798,0.00001493748,0.9956366,0.002170311,0.00007949204,0.0005747052,0.0002331999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00009721261,0.00001996578,0.99757,0.0007871006,0.0006961376,0.0003293543,0.00007305481,0.0003529902,0.0000741257],"genre_scores_gemma":[0.6556898,0.00006844183,0.3422813,0.0002611337,0.00002920513,0.0001006244,0.0000172001,0.00002630415,0.001525933],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6555926,"threshold_uncertainty_score":0.820176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03676807627206966,"score_gpt":0.2553220708945491,"score_spread":0.2185539946224795,"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."}}