{"id":"W1605270666","doi":"10.1109/iat.2004.115","title":"Using event-streams for fault-management in MAS","year":2004,"lang":"en","type":"article","venue":"IEEE/WIC/ACM International Conference on Intelligent Agent Technology","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Dependability; Computer science; Software deployment; Fault management; Event (particle physics); Component (thermodynamics); Key (lock); Fault (geology); Domain (mathematical analysis); Distributed computing; Computer security; Software engineering; Engineering","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":[0.002988657,0.0006572914,0.0006310759,0.0009620269,0.0006494373,0.002101479,0.001189854,0.001065771,0.002072222],"category_scores_gemma":[0.007069611,0.0002902812,0.0004037532,0.0007556559,0.0008972518,0.003242212,0.0009825689,0.001151907,0.000468983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008811157,"about_ca_system_score_gemma":0.0006045296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001745549,"about_ca_topic_score_gemma":0.001470435,"domain_scores_codex":[0.9984188,0.0007300927,0.0001755595,0.000180589,0.000407228,0.00008776796],"domain_scores_gemma":[0.9949813,0.003212127,0.0004260769,0.0006075835,0.0005934794,0.0001793514],"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":[0.001817232,0.0006014003,0.005339792,0.0006717345,0.0001727821,0.0008397386,0.001226301,0.4641113,0.02631248,0.1031764,0.004668989,0.3910619],"study_design_scores_gemma":[0.0001500823,0.0002096821,0.0004332633,0.00004650207,0.00006183503,0.0001305787,0.00008229364,0.9221007,0.02453201,0.04210722,0.01010241,0.00004341217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03543668,0.0004989055,0.9570925,0.0003801033,0.0001397529,0.0002381213,0.0001279825,0.004007674,0.002078336],"genre_scores_gemma":[0.6845491,0.0005953852,0.3122626,0.0001529964,0.0001154543,0.000264253,0.0002358353,0.000155752,0.001668685],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002988657,"threshold_uncertainty_score":0.01580566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1050961698613885,"score_gpt":0.3592966047063397,"score_spread":0.2542004348449513,"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."}}