{"id":"W2777581989","doi":"10.1111/coin.12155","title":"A hybrid trust model using reinforcement learning and fuzzy logic","year":2017,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Access Control and Trust","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Reinforcement learning; Fuzzy logic; Trustworthiness; Term (time); Artificial intelligence; Machine learning; Computer security","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0002927842,0.00007776174,0.00009781102,0.00003780379,0.002104852,0.0003410169,0.0002606388,0.00002985461,0.00005121421],"category_scores_gemma":[0.0002941228,0.0000767196,0.00003132795,0.0000272847,0.0003618538,0.0003663756,0.0001100332,0.0001078307,0.00002300454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004704885,"about_ca_system_score_gemma":0.0001239155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005159416,"about_ca_topic_score_gemma":0.00002196388,"domain_scores_codex":[0.9991894,0.00003348659,0.0001530786,0.000179103,0.0002615026,0.0001833712],"domain_scores_gemma":[0.9994602,0.0000891466,0.0001507453,0.00008237906,0.0001413748,0.00007613296],"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":[0.000007353364,0.000006997991,0.002128444,0.000003556146,0.000007672282,0.000003955882,0.0007029051,0.7727665,0.000005194327,0.2099624,0.000009691461,0.01439536],"study_design_scores_gemma":[0.00004642609,0.00001053007,0.0005215934,0.00001211878,0.000006442024,0.000001843609,0.0002476264,0.794706,0.00001329408,0.2038876,0.0004601697,0.00008629153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07421868,0.0001869841,0.878873,0.0005835888,0.0001406883,0.0001490453,0.00000151387,0.00004501852,0.04580149],"genre_scores_gemma":[0.9932774,0.00005005105,0.005593942,0.0001592932,0.0001050181,0.000004166841,0.000002551745,0.000004859204,0.0008027524],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9190587,"threshold_uncertainty_score":0.9991943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09582395210740642,"score_gpt":0.3850028450483746,"score_spread":0.2891788929409682,"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."}}