{"id":"W1794176462","doi":"10.48550/arxiv.1301.2304","title":"Vector-space Analysis of Belief-state Approximation for POMDPs","year":2013,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Approximation algorithm; State space; Approximation error; Mathematical optimization; Space (punctuation); State (computer science); Quality (philosophy); Value (mathematics); Artificial intelligence; Algorithm; Mathematics; Machine learning","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":[],"consensus_categories":[],"category_scores_codex":[0.0001300734,0.00009764863,0.0001823077,0.0002548125,0.00007203266,0.00004699888,0.0005199484,0.00004717395,0.0000155088],"category_scores_gemma":[0.00001619809,0.0001038029,0.0001357957,0.001307962,0.00003232518,0.0005084101,0.00008931443,0.0000553875,0.00002741368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003860057,"about_ca_system_score_gemma":0.00003848181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001625171,"about_ca_topic_score_gemma":0.00001756737,"domain_scores_codex":[0.9992458,0.00003702628,0.0001258242,0.0003504497,0.0000533186,0.0001875531],"domain_scores_gemma":[0.9990613,0.00008489248,0.0001212039,0.0004269869,0.0002256837,0.00007987304],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001182396,0.0001352647,0.002452322,0.00003519359,0.0004556154,0.000003619752,0.0007491698,0.1668524,0.001861217,0.824429,0.0002721885,0.002742181],"study_design_scores_gemma":[0.0001757094,0.00005083573,0.003433568,0.000005710547,0.0001198178,2.134111e-7,0.00002644241,0.9709336,0.0008189633,0.02427262,0.00004134189,0.0001212383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2552266,0.000008278487,0.7440115,0.00008471317,0.00003586533,0.0001178809,0.000003425683,0.00007951719,0.0004322278],"genre_scores_gemma":[0.9857143,0.00001438468,0.01372936,0.00003978218,0.000006913265,0.000001388315,0.000004761561,0.000004308646,0.0004847788],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8040811,"threshold_uncertainty_score":0.4232958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05598216329112023,"score_gpt":0.1835560925630462,"score_spread":0.127573929271926,"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."}}