{"id":"W2077371739","doi":"10.1109/iros.2005.1545150","title":"Enhanced learning classifier system for robot navigation","year":2005,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Sonar; Mobile robot; Obstacle avoidance; Computer science; Artificial intelligence; Robot; Classifier (UML); Maxima and minima; Mobile robot navigation; Obstacle; Robot learning; Machine learning; Robot control; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000104127,0.00005173465,0.00005380462,0.00002279972,0.0002064096,0.00005154395,0.0002131022,0.00003055421,0.000007231662],"category_scores_gemma":[0.00000497756,0.00004731757,0.00003522625,0.000137704,0.000009312443,0.0003138963,0.00003541344,0.00005146379,0.0001069864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005633702,"about_ca_system_score_gemma":0.00002226111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003438296,"about_ca_topic_score_gemma":0.00000142033,"domain_scores_codex":[0.9994655,0.00001215343,0.0001226766,0.0001899556,0.00008610785,0.0001235565],"domain_scores_gemma":[0.9996322,0.00004733398,0.00004381481,0.0001641392,0.00007477977,0.00003772998],"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":[0.000001375539,0.00003889505,0.0000137821,0.00001742912,0.000006730473,1.348566e-7,0.0001756948,0.0156277,0.01538009,0.8153476,0.001094213,0.1522963],"study_design_scores_gemma":[0.0001920919,0.00002811856,0.0003303401,0.00001450007,0.000002291722,0.000005663716,0.00007404945,0.9445164,0.01962816,0.0006780245,0.03442965,0.000100706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001430715,0.00002385271,0.9864125,0.001359154,0.00007348079,0.0001629475,5.22321e-7,0.000283489,0.01025336],"genre_scores_gemma":[0.6722453,9.858486e-7,0.3242369,0.00004143946,0.0001307069,0.0001015855,0.000006409997,0.000003017585,0.003233559],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9288887,"threshold_uncertainty_score":0.1929554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01642719527968382,"score_gpt":0.2598579548441792,"score_spread":0.2434307595644954,"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."}}