{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008067584,0.0008851253,0.000990935,0.0005960719,0.0006082704,0.001276145,0.001128698,0.0009670003,0.002788064],"category_scores_gemma":[0.002453001,0.0005287708,0.001055917,0.001192223,0.001287038,0.002053238,0.002120452,0.00166985,0.0004395979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001324839,"about_ca_system_score_gemma":0.001487052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005307382,"about_ca_topic_score_gemma":0.004922368,"domain_scores_codex":[0.9990939,0.0002920786,0.0000516922,0.0002329487,0.0002318284,0.00009757989],"domain_scores_gemma":[0.9990625,0.0006316945,0.00006516354,0.0001147525,0.00008311794,0.00004278562],"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":[0.00003994374,0.00001724467,0.0002631434,0.0001116913,0.00003172912,0.00006117296,0.00006553068,0.8158741,0.0008138553,0.1446035,0.001697338,0.03642072],"study_design_scores_gemma":[0.000006670296,0.0000159817,0.00006056748,0.00000942907,0.000007918831,0.0000190694,0.00001583767,0.8773876,0.0004732256,0.1198366,0.002159662,0.000007363354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004869794,0.0001795274,0.9926103,0.0001188277,0.00001568594,0.00001852146,0.00009530701,0.0002120701,0.001880029],"genre_scores_gemma":[0.3482021,0.000725998,0.6461261,0.0001280918,0.00005173984,0.000167616,0.0005743835,0.0001850699,0.003838964],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005307382,"threshold_uncertainty_score":0.01055294,"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."}}