{"id":"W2860358344","doi":"10.1162/evco_a_00301","title":"Evolving Multimodal Robot Behavior via Many Stepping Stones with the Combinatorial Multiobjective Evolutionary Algorithm","year":2021,"lang":"en","type":"preprint","venue":"Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Science Foundation","keywords":"Leverage (statistics); Artificial intelligence; Evolutionary robotics; Robotics; Computer science; Reinforcement learning; Evolutionary algorithm; Task (project management); Selection (genetic algorithm); Machine learning; Process (computing); Robot; Mathematical optimization; 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":[0.00105123,0.0008981062,0.0008645065,0.0006807947,0.0003835109,0.0007243856,0.001001734,0.0009491429,0.001419079],"category_scores_gemma":[0.002158269,0.0003990502,0.0008038312,0.0004597433,0.0008699122,0.0006189771,0.0009958873,0.0008275022,0.000186893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006259296,"about_ca_system_score_gemma":0.0007911285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002116641,"about_ca_topic_score_gemma":0.001859338,"domain_scores_codex":[0.9996589,0.0001667291,0.00001602365,0.00004723968,0.00007820056,0.00003291878],"domain_scores_gemma":[0.9994325,0.0003546715,0.00005416415,0.00004892142,0.00006705265,0.00004263516],"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":[0.00001314704,0.00002175871,0.0003010227,0.00002080548,0.00002725201,0.000037933,0.00002706157,0.9798957,0.001010517,0.007192671,0.0001786457,0.01127352],"study_design_scores_gemma":[0.000006900445,0.00001432347,0.00004555967,0.000003036863,0.000003825736,0.000006716547,0.000004463904,0.9971734,0.0001351596,0.002410424,0.0001936632,0.000002583273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05799733,0.0002589624,0.9365528,0.0002254206,0.00003936907,0.00008805527,0.00002399657,0.0002369237,0.004577145],"genre_scores_gemma":[0.555726,0.0002262785,0.4403898,0.000179648,0.00002557045,0.0004831807,0.00007935884,0.00008747833,0.002802816],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002116641,"threshold_uncertainty_score":0.005559504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01092838049621494,"score_gpt":0.2596300563978829,"score_spread":0.2487016759016679,"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."}}