{"id":"W35753949","doi":"10.1111/ele.14058","title":"Modeling a fault tolerant multiagent system for the control of a mobile robot using MaSE methodology","year":2006,"lang":"en","type":"article","venue":"ACOS'06 Proceedings of the 5th WSEAS international conference on Applied computer science","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University; Center for Makroøkologi, Evolution og Klima; University of Manitoba","keywords":"Robot; Mobile robot; Fault tolerance; Control system; Reliability (semiconductor); Multi-agent system; Computer science; Control engineering; Embedded system; Distributed computing; Engineering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007148219,0.0008471899,0.0007259384,0.000472096,0.0006336445,0.0011424,0.00117546,0.001468122,0.003618464],"category_scores_gemma":[0.001643142,0.0004425752,0.0006783113,0.0003238197,0.0008343863,0.000742579,0.001015374,0.0009719478,0.0004667742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008034984,"about_ca_system_score_gemma":0.0009041193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01117461,"about_ca_topic_score_gemma":0.007431456,"domain_scores_codex":[0.9997111,0.00008086568,0.00001978462,0.0000696901,0.00006614457,0.00005236355],"domain_scores_gemma":[0.9992403,0.0004196952,0.0001397783,0.00003272608,0.0001139006,0.00005368943],"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":[0.00001813629,0.000007448602,0.0001498771,0.00002184473,0.00001176066,0.0000615535,0.00002761143,0.9949616,0.0003961059,0.002069296,0.00008390585,0.002190841],"study_design_scores_gemma":[0.000006641565,0.00001307117,0.00003111095,0.000002126776,0.000003133252,0.000005612442,0.000004825165,0.9989898,0.00006048041,0.000744023,0.0001374722,0.000001680175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04166188,0.0002560338,0.9512934,0.000378252,0.00006840586,0.0001114082,0.0001047044,0.000513998,0.005611892],"genre_scores_gemma":[0.9382229,0.0002079566,0.0553667,0.00006377776,0.00003377544,0.0003949242,0.00009223584,0.00004116969,0.005576512],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01117461,"threshold_uncertainty_score":0.02221918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05892950777376385,"score_gpt":0.2947631496691855,"score_spread":0.2358336418954216,"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."}}