{"id":"W4381512810","doi":"10.21203/rs.3.rs-3040613/v1","title":"A framework for creating systems capable of adapting and dealing with norms based on software agents","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Normative; Adaptation (eye); Computer science; Software agent; Key (lock); Intelligent agent; Agent architecture; Java; Human–computer interaction; Software; Software architecture; Multi-agent system; Software engineering; Product (mathematics); Knowledge management; Process management; Artificial intelligence; Computer security; Engineering; Psychology; Programming language","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":[0.004879734,0.0006246123,0.0005063197,0.001083729,0.001305411,0.003283035,0.002729989,0.001862915,0.002400856],"category_scores_gemma":[0.004559138,0.0006181146,0.001395492,0.0004750682,0.003710084,0.003129137,0.004001131,0.001815744,0.000605012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001184866,"about_ca_system_score_gemma":0.002272696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003587683,"about_ca_topic_score_gemma":0.003351833,"domain_scores_codex":[0.9978837,0.0009232334,0.0001913711,0.0002783271,0.0005358958,0.0001873719],"domain_scores_gemma":[0.9979308,0.0008025999,0.0001875533,0.0004915674,0.0002928891,0.0002945693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006357999,0.0001757096,0.00134663,0.0001755577,0.00006055631,0.0005404379,0.001519432,0.07156377,0.009031052,0.8793517,0.001417252,0.03475434],"study_design_scores_gemma":[0.0001202096,0.0001838945,0.0004326941,0.0002281448,0.00008963013,0.0004693883,0.0004498334,0.5697538,0.008718557,0.2941421,0.1253262,0.00008562401],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01202441,0.0001558727,0.9799386,0.0003738714,0.00005687909,0.0002534147,0.00002715987,0.00145729,0.0057125],"genre_scores_gemma":[0.1742366,0.000246557,0.8209117,0.0001220999,0.00003518757,0.0004642906,0.0001235171,0.0001858148,0.003674228],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004879734,"threshold_uncertainty_score":0.02580678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2000411548678933,"score_gpt":0.4142477728238909,"score_spread":0.2142066179559976,"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."}}