{"id":"W2951833857","doi":"10.48550/arxiv.1303.5740","title":"High Level Path Planning with Uncertainty","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motion planning; Shortest path problem; Computer science; Longest path problem; Path (computing); Mathematical optimization; Graph; Markov decision process; Any-angle path planning; Process (computing); Markov process; Theoretical computer science; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001801844,0.0002814314,0.000274684,0.0002167032,0.00017919,0.0002601577,0.001640012,0.0002023021,0.00008878039],"category_scores_gemma":[0.00001491163,0.0002642436,0.00008227178,0.0004854205,0.0001005842,0.0004634116,0.001453566,0.0005842607,0.000137275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001557073,"about_ca_system_score_gemma":0.0002901287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005898663,"about_ca_topic_score_gemma":0.00001488682,"domain_scores_codex":[0.9982509,0.0001236445,0.0001502256,0.0009399459,0.0001400828,0.0003951752],"domain_scores_gemma":[0.9982449,0.00006955797,0.0001889288,0.001006,0.0002683076,0.0002222682],"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":[0.00001001053,0.00003455987,0.0008505274,0.00002865304,0.00004249219,0.0001403475,0.000229591,0.8350392,0.000002840768,0.1625125,0.0008432998,0.0002660049],"study_design_scores_gemma":[0.0005653472,0.00008408553,0.001160042,0.0001466261,0.00001719194,0.000004132733,0.00006631664,0.9753592,0.00001589924,0.02149458,0.0006416792,0.0004449069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07262405,0.00001739499,0.9236766,0.0001814759,0.0002454565,0.0003370734,0.00001453933,0.0002937466,0.002609741],"genre_scores_gemma":[0.9701765,0.00004667679,0.02522049,0.0001750359,0.00004158131,0.000002167947,0.00003280937,0.00001732831,0.004287374],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.898456,"threshold_uncertainty_score":0.999981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1240046288587581,"score_gpt":0.1975235675809306,"score_spread":0.07351893872217255,"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."}}