{"id":"W1530346371","doi":"10.1007/978-3-540-72530-5_58","title":"Robotic Target Tracking with Approximation Space-Based Feedback During Reinforcement Learning","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Power Line Inspection Robots","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Crawling; Computer science; Reinforcement learning; Artificial intelligence; Robot; Tracking (education); Computer vision; Line (geometry); Simulation; 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":[0.0005943267,0.000518263,0.0006059186,0.0001886638,0.0002398521,0.0004896387,0.0008825959,0.0006383078,0.001564942],"category_scores_gemma":[0.001825286,0.0002983422,0.0003034683,0.0002065863,0.0005431838,0.0006412397,0.0008476288,0.0009534273,0.0003139712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004142213,"about_ca_system_score_gemma":0.0004264623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002508176,"about_ca_topic_score_gemma":0.00206178,"domain_scores_codex":[0.9997403,0.00006054046,0.00001204976,0.00005172061,0.00009968648,0.00003570766],"domain_scores_gemma":[0.9993773,0.0003333516,0.00006018764,0.00007617854,0.0001191294,0.00003384203],"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":[0.0003193505,0.000106528,0.0005010617,0.0000932379,0.00004205063,0.00008783902,0.0001120139,0.7860051,0.02112433,0.00872325,0.00134506,0.1815401],"study_design_scores_gemma":[0.000008726652,0.00004233422,0.00007862823,0.000003531977,0.000003503582,0.00001845937,0.000002114199,0.9966859,0.001579193,0.001346604,0.0002278701,0.00000307192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02225332,0.0001965049,0.9741189,0.00006766158,0.0000477414,0.00002533711,0.00001196815,0.0005827425,0.002695952],"genre_scores_gemma":[0.9024959,0.0001008686,0.09477609,0.0000479728,0.00002590754,0.0000645794,0.00003144927,0.00005527483,0.002401916],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002508176,"threshold_uncertainty_score":0.005235255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01289045167526322,"score_gpt":0.216068399926788,"score_spread":0.2031779482515247,"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."}}