{"id":"W7124135461","doi":"10.65109/gnlj3027","title":"POMDP planning and execution in an augmented space","year":2014,"lang":"","type":"article","venue":"","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Upper and lower bounds; Markov decision process; Partially observable Markov decision process; Suite; Linear programming; Action (physics); Space (punctuation); Branch and bound","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":[0.0009675822,0.0009522439,0.0007066284,0.0004364561,0.0005887842,0.001262239,0.0008711665,0.0008058245,0.003365216],"category_scores_gemma":[0.002766774,0.0006311703,0.0009759287,0.000551309,0.001439528,0.001716687,0.001493554,0.001520184,0.0003940338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001064587,"about_ca_system_score_gemma":0.002108717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00886674,"about_ca_topic_score_gemma":0.009443981,"domain_scores_codex":[0.999127,0.000259424,0.0000650772,0.0001708389,0.0002425343,0.0001352845],"domain_scores_gemma":[0.9988785,0.0007072649,0.00009569289,0.0001575413,0.0001111904,0.00004971987],"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":[0.0001132828,0.00003064549,0.0003253433,0.0000880671,0.00001974352,0.00009236873,0.00009300516,0.9520602,0.001992458,0.02743413,0.0004370255,0.01731381],"study_design_scores_gemma":[0.00001976132,0.00003056126,0.0000861883,0.00001238114,0.000007901969,0.00001380834,0.00002425779,0.9753395,0.001980132,0.02094194,0.001536917,0.000006645437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04608389,0.0001745179,0.9455792,0.0002526103,0.00004341423,0.000117382,0.00033241,0.002173418,0.005243092],"genre_scores_gemma":[0.5591576,0.0002371523,0.4372787,0.0000682744,0.00001904888,0.0002778362,0.0003858886,0.0001557958,0.002419676],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00886674,"threshold_uncertainty_score":0.01763022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02278674210685786,"score_gpt":0.2779878417307917,"score_spread":0.2552010996239338,"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."}}