{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007653299,0.0001624292,0.0002088091,0.0001432555,0.000135059,0.0000770253,0.0006077694,0.00003186958,0.00003821898],"category_scores_gemma":[0.0001927552,0.0001302336,0.00005612896,0.0005704694,0.0001055989,0.0001740016,0.0001141749,0.0002195904,0.00004157802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003292766,"about_ca_system_score_gemma":0.00006988105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006176349,"about_ca_topic_score_gemma":0.0001135092,"domain_scores_codex":[0.9977811,0.0005158397,0.0008140565,0.000345481,0.0003170818,0.0002264416],"domain_scores_gemma":[0.9987073,0.0001524335,0.0004332571,0.0003400327,0.0003306941,0.00003630354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001824412,0.0005603856,0.01799333,0.00002302298,0.000005696395,0.00004459731,0.00238606,0.03466872,0.6866975,0.1649485,0.000006616993,0.09264726],"study_design_scores_gemma":[0.00008610122,0.0005274827,0.1198196,0.0004206885,0.00002864525,0.00001264579,0.000918644,0.4814421,0.395918,0.0003523822,0.00004949558,0.0004241883],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7645168,0.00002702031,0.2321221,0.00004103284,0.0002294327,0.000372257,0.000002669787,0.00003330382,0.002655361],"genre_scores_gemma":[0.997306,0.000004907755,0.002531803,0.0000114526,0.00005520464,0.00001082266,0.00000198347,0.0000129315,0.00006484189],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4467734,"threshold_uncertainty_score":0.5310773,"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."}}