{"id":"W2157286417","doi":"10.1109/robot.2003.1242037","title":"Path planning using learned constraints and preferences","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Motion planning; Path (computing); Mathematical optimization; Artificial intelligence; Robot; Mathematics; Computer network","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":[0.001026255,0.0006645091,0.000821099,0.0006966501,0.0005697333,0.000725014,0.001461487,0.0007766056,0.002358534],"category_scores_gemma":[0.005054523,0.0007487133,0.0006890193,0.0008681063,0.0008829966,0.002363287,0.001168805,0.001122903,0.0003836177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008411053,"about_ca_system_score_gemma":0.001266659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003568449,"about_ca_topic_score_gemma":0.007553225,"domain_scores_codex":[0.9991592,0.0002019256,0.0000457856,0.0002790274,0.0002589099,0.0000551948],"domain_scores_gemma":[0.998197,0.001056526,0.0001855778,0.0002559645,0.0002213553,0.00008351413],"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":[0.0001355301,0.00006970207,0.001277415,0.0001538544,0.0000722528,0.0001095598,0.0002166223,0.7777349,0.005566012,0.02701338,0.001126949,0.1865237],"study_design_scores_gemma":[0.00001461371,0.00003075821,0.0001765788,0.000009661035,0.00000705197,0.00003427735,0.00001770601,0.9757943,0.001551694,0.02161147,0.0007406214,0.00001126994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01013017,0.00004633172,0.9889259,0.00004664276,0.000005409476,0.00002375514,0.00003484228,0.0002043249,0.0005826938],"genre_scores_gemma":[0.3503843,0.0001592516,0.6465608,0.00009305149,0.00002373465,0.0002161713,0.0002350529,0.0002273408,0.002100249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003568449,"threshold_uncertainty_score":0.007890046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07997498138627972,"score_gpt":0.3062817273573546,"score_spread":0.2263067459710749,"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."}}