{"id":"W2090744467","doi":"10.1109/icra.2014.6907405","title":"Maximizing visibility in collaborative trajectory planning","year":2014,"lang":"en","type":"article","venue":"","topic":"Guidance and Control Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pursuer; Visibility; Robot; Computer science; Tree traversal; Context (archaeology); Trajectory; Planner; Motion planning; Artificial intelligence; Distributed computing; Mathematical optimization; Computer vision; Mathematics; Algorithm","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":[],"consensus_categories":[],"category_scores_codex":[0.0002296747,0.00007575765,0.0001413315,0.00004591252,0.00001548103,0.00001763421,0.00005919462,0.00004024043,0.00002089578],"category_scores_gemma":[0.00002161935,0.00007018453,0.00001774143,0.000144223,0.000007161592,0.00008615434,0.000005219285,0.00007472056,0.0000286259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004368829,"about_ca_system_score_gemma":0.000007302035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003007748,"about_ca_topic_score_gemma":0.0001139214,"domain_scores_codex":[0.9994898,0.00003263921,0.0001584043,0.0001017724,0.0000637764,0.0001535971],"domain_scores_gemma":[0.999776,0.00004910488,0.00001019709,0.0001204528,0.00001565353,0.00002857763],"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":[0.00006898632,0.00007571044,0.2368061,0.0003595063,0.00009935575,0.00002646744,0.009553867,0.6198182,0.08295428,0.009556014,0.00488187,0.03579968],"study_design_scores_gemma":[0.002480578,0.00009889634,0.2429603,0.0002067479,0.000009678359,0.000003833714,0.003288859,0.7202711,0.007319893,0.001314572,0.02133082,0.0007147299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8444402,0.0004611978,0.02066998,0.00002251874,0.0002733458,0.0001622524,0.000001171722,0.0002968437,0.1336726],"genre_scores_gemma":[0.9992831,0.000001250447,0.0005146353,0.00003654284,0.00006203402,0.00001931016,7.505376e-7,0.00001007528,0.00007231387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.154843,"threshold_uncertainty_score":0.2862042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006979171347625286,"score_gpt":0.2128131469796885,"score_spread":0.2058339756320632,"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."}}