{"id":"W2023014747","doi":"10.1145/1143997.1144275","title":"Genetic algorithms for action set selection across domains","year":2006,"lang":"en","type":"article","venue":"","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Action selection; Software portability; Interpretation (philosophy); Selection (genetic algorithm); Set (abstract data type); Domain (mathematical analysis); Action (physics); Markov decision process; Genetic algorithm; Image (mathematics); Artificial intelligence; Algorithm; Machine learning; Mathematical optimization; Markov process; Mathematics","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":[0.002898697,0.001476003,0.001539543,0.001769069,0.0008264017,0.001198281,0.001707081,0.001755996,0.002720486],"category_scores_gemma":[0.006625036,0.0007335365,0.0009859857,0.001194285,0.001700393,0.001094439,0.001468725,0.002059761,0.0004584252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001950779,"about_ca_system_score_gemma":0.0017718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008687341,"about_ca_topic_score_gemma":0.006563341,"domain_scores_codex":[0.9988509,0.0005372712,0.00006658783,0.000212664,0.0002247783,0.0001077996],"domain_scores_gemma":[0.9965665,0.002765301,0.000167303,0.0001490418,0.0002602031,0.00009160334],"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":[0.00003064987,0.00003378212,0.0003014021,0.00002813684,0.0000434532,0.00002771922,0.00003912633,0.9567555,0.0002697998,0.01080333,0.0004641634,0.03120296],"study_design_scores_gemma":[0.00002176955,0.00001585524,0.00004389403,0.000006695189,0.000007609236,0.000006961603,0.000007405628,0.9895836,0.0001475976,0.009828231,0.000326444,0.000004035142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01797505,0.0003727144,0.9776785,0.0002728684,0.00003864114,0.0001237368,0.00005934427,0.0004962996,0.002982844],"genre_scores_gemma":[0.455115,0.0004709357,0.5393975,0.0002740399,0.000069715,0.0007687817,0.0002714293,0.0001912664,0.00344127],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008687341,"threshold_uncertainty_score":0.01727355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01335363358263963,"score_gpt":0.2641009589134044,"score_spread":0.2507473253307648,"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."}}