{"id":"W3035407439","doi":"10.24963/ijcai.2020/44","title":"Combining Direct Trust and Indirect Trust in Multi-Agent Systems","year":2020,"lang":"en","type":"article","venue":"","topic":"Access Control and Trust","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Trustworthiness; Variety (cybernetics); Multi-agent system; Trust management (information system); Computational trust; Computer security; Reputation; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004319802,0.0001063043,0.0002671753,0.00005418567,0.0002465353,0.0001810215,0.0001742113,0.00008285141,0.000124943],"category_scores_gemma":[0.000239297,0.00009128996,0.00003372714,0.0003151372,0.0001094012,0.0002175368,0.00005705097,0.0001243842,0.00003772794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004278454,"about_ca_system_score_gemma":0.00005790119,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007720652,"about_ca_topic_score_gemma":0.0024498,"domain_scores_codex":[0.9988335,0.0001890426,0.0002128075,0.0002572062,0.0002202989,0.0002871844],"domain_scores_gemma":[0.9995223,0.000121826,0.00005955667,0.00006915959,0.00002488654,0.0002023231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004917697,0.0001124187,0.9282565,0.00005505573,0.00005418672,0.00006392749,0.03860216,0.0001104056,0.0001066982,0.0190018,0.0007345004,0.01285321],"study_design_scores_gemma":[0.01118449,0.0003281539,0.5069228,0.0002128187,0.0001228675,0.000004253658,0.1109656,0.09702048,0.0002933791,0.0002087294,0.2709284,0.001807991],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6287945,0.001788534,0.0003615356,0.00403944,0.0005547618,0.0007516422,0.000009943226,0.0003309062,0.3633687],"genre_scores_gemma":[0.9978498,0.0001117561,0.00009558197,0.0004086157,0.0001201792,0.000024328,0.000001540929,0.000009358711,0.001378902],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4213336,"threshold_uncertainty_score":0.998887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06420866260327983,"score_gpt":0.3054620315744345,"score_spread":0.2412533689711546,"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."}}