{"id":"W2012068092","doi":"10.1145/2001858.2002106","title":"Bayesian networks learning for strategies in artificial life","year":2011,"lang":"en","type":"article","venue":"","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bayesian network; Artificial intelligence; Artificial life; Computer science; Machine learning; Process (computing); Bayesian probability; Representation (politics); Graphical model; Evolutionary algorithm; Evolutionary computation","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004058073,0.001406304,0.00143505,0.002220812,0.0008558126,0.002661751,0.002004707,0.002920548,0.00528774],"category_scores_gemma":[0.01842278,0.0008376122,0.001393281,0.002249953,0.003085889,0.004545473,0.001649582,0.003867278,0.0007913682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003582443,"about_ca_system_score_gemma":0.001598249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008912942,"about_ca_topic_score_gemma":0.008694411,"domain_scores_codex":[0.9979659,0.001265076,0.00009518061,0.0002669636,0.0003413246,0.00006555026],"domain_scores_gemma":[0.9926677,0.006254953,0.0003702413,0.0002229739,0.0003298995,0.0001541687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002042058,0.00003004939,0.0005354848,0.0001589564,0.00007521735,0.00004749684,0.000133819,0.2780555,0.0002189945,0.6828766,0.001697334,0.03615003],"study_design_scores_gemma":[0.00001011208,0.000008686659,0.00008358242,0.00003584695,0.000009260403,0.00001246532,0.00001276619,0.405818,0.00006560197,0.5917795,0.002150248,0.00001395384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003382472,0.002321335,0.9882399,0.001534345,0.00008658334,0.00003911426,0.0001055446,0.000115006,0.004175746],"genre_scores_gemma":[0.3618313,0.008161453,0.6171235,0.000810096,0.0006911167,0.0008441788,0.0005385241,0.0001951408,0.009804655],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008912942,"threshold_uncertainty_score":0.02599257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06801594693876549,"score_gpt":0.2692295468228511,"score_spread":0.2012135998840855,"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."}}