{"id":"W2150279955","doi":"10.1109/caia.1995.378785","title":"Training agents in a complex environment","year":2002,"lang":"en","type":"article","venue":"","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Natural Resources Canada","funders":"Ministry of Environment","keywords":"Computer science; Variety (cybernetics); Plan (archaeology); Set (abstract data type); Generalization; Visualization; Resource (disambiguation); Cover (algebra); Task (project management); Intelligent agent; Granularity; Artificial intelligence; Systems engineering; Engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001024058,0.00005172498,0.00006875998,0.00005045308,0.00002879143,0.00003181681,0.0001948464,0.00001864387,0.0009244764],"category_scores_gemma":[0.000002874828,0.00004598165,0.00001960606,0.00007131087,0.000005326795,0.000154899,0.00004748393,0.00003000056,0.000451404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003337509,"about_ca_system_score_gemma":0.000001454137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000419822,"about_ca_topic_score_gemma":0.00001132405,"domain_scores_codex":[0.9993955,0.00003019715,0.0001395031,0.0001653428,0.0001366317,0.0001328638],"domain_scores_gemma":[0.9997413,0.00001236129,0.0000297744,0.0001781438,0.00000206976,0.00003641639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002836543,0.0008740355,0.03352613,0.00004543088,0.00003789126,0.0001138034,0.05404466,0.004372702,0.009404484,0.07298958,0.03649532,0.7880931],"study_design_scores_gemma":[0.0003321589,0.00001667063,0.08995982,0.000005720409,4.569895e-7,0.000003703345,0.00005161948,0.8776133,0.00007508573,0.00007858162,0.03176873,0.00009413964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1136054,0.00006968022,0.8208883,0.001363145,0.0002317338,0.0003577129,8.418438e-7,0.0001455357,0.06333764],"genre_scores_gemma":[0.9866893,0.000008457088,0.01170745,0.0002698631,0.00001731358,0.000006861792,7.985271e-7,0.000002344786,0.001297662],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8732406,"threshold_uncertainty_score":0.9999888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2595558463893733,"score_gpt":0.2838193188081893,"score_spread":0.02426347241881605,"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."}}