{"id":"W6301683","doi":"10.1016/0092-8674(83)90302-1","title":"Testing the limits of emergent behavior in MAS using learning of cooperative behavior","year":2006,"lang":"en","type":"article","venue":"European Conference on Artificial Intelligence","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Test (biology); Class (philosophy); Artificial intelligence; Multi-agent system; Intelligent agent; Human–computer interaction","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":[0.01005692,0.0004391916,0.0004640524,0.0006239087,0.0005756228,0.001464687,0.0009581033,0.0009918441,0.001383492],"category_scores_gemma":[0.06540854,0.0003426593,0.0003771671,0.0002787455,0.002620141,0.003070864,0.00197226,0.001205363,0.0001081065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001115607,"about_ca_system_score_gemma":0.0007954722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001226938,"about_ca_topic_score_gemma":0.0007004227,"domain_scores_codex":[0.9946431,0.003552912,0.000234004,0.0004952456,0.0006729131,0.0004017423],"domain_scores_gemma":[0.8933727,0.09070778,0.005767865,0.005408156,0.002068104,0.002675483],"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":[0.004731845,0.003380819,0.1857084,0.0006300174,0.000662516,0.0009144036,0.00414044,0.5857313,0.0336119,0.0952293,0.0005744424,0.08468469],"study_design_scores_gemma":[0.0001236893,0.001375281,0.01028197,0.00002795347,0.0000430845,0.0001142052,0.000647801,0.9370456,0.00655865,0.04340613,0.0003421016,0.00003354503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9818246,0.00001892172,0.01588237,0.000164131,0.000003428548,0.000029919,0.00001521813,0.00004076173,0.002020728],"genre_scores_gemma":[0.9959123,0.00001197618,0.003905869,0.0000127521,0.000002459644,0.00003368695,0.00001387841,0.000005181099,0.0001019084],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01005692,"threshold_uncertainty_score":0.05318677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2503280750124302,"score_gpt":0.3455471282710017,"score_spread":0.09521905325857155,"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."}}