{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008781226,0.0002454076,0.0002429104,0.0002605147,0.0001729241,0.0005725329,0.0005030591,0.0001376205,0.00005552824],"category_scores_gemma":[0.0001132698,0.00025445,0.00002688152,0.0004331112,0.00009568143,0.001144667,0.0003996471,0.000323927,0.00004844892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008056979,"about_ca_system_score_gemma":0.00004583396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009158142,"about_ca_topic_score_gemma":0.00001355305,"domain_scores_codex":[0.9978034,0.0002584288,0.0004174346,0.0006095626,0.0003807317,0.0005304469],"domain_scores_gemma":[0.9988503,0.000119763,0.0001692911,0.0005900331,0.0000631565,0.0002074274],"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":[0.00001740364,0.00004292482,0.04744987,0.00003531878,0.00000830401,0.000009862194,0.003277068,0.8948178,0.0005483777,0.04280905,0.0001548847,0.01082916],"study_design_scores_gemma":[0.0007098913,0.0005730701,0.03853338,0.0001453073,0.000006656033,0.000008830716,0.0002072846,0.9577999,0.000293186,0.0001884802,0.001241206,0.0002928847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07486417,0.00008416669,0.9127872,0.0006742692,0.0004414955,0.0002016793,1.179298e-7,0.00009930935,0.01084759],"genre_scores_gemma":[0.9730096,0.00001661788,0.02421963,0.0003633706,0.00007742084,0.000003200405,0.000003365287,0.00001354411,0.002293179],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8981455,"threshold_uncertainty_score":0.9999908,"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."}}