{"id":"W2128371572","doi":"10.65109/yget7348","title":"Point-based incremental pruning heuristic for solving finite-horizon DEC-POMDPs","year":2009,"lang":"en","type":"preprint","venue":"","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Partially observable Markov decision process; Computer science; Backup; Pruning; Heuristics; Mathematical optimization; Heuristic; Computation; Markov decision process; Bounded function; Markov process; Algorithm; Artificial intelligence; Markov chain; Machine learning; Mathematics; Markov model","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":[0.00132042,0.0007213015,0.001402754,0.0006555995,0.0004222665,0.0005760567,0.001189592,0.0008159784,0.001302722],"category_scores_gemma":[0.003997515,0.0005279623,0.0005621883,0.0006082481,0.0006353505,0.0008207948,0.0009008016,0.001089459,0.0001852046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007064336,"about_ca_system_score_gemma":0.001334856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003862241,"about_ca_topic_score_gemma":0.004555684,"domain_scores_codex":[0.9994982,0.0001798602,0.0000271517,0.00005852844,0.0001526889,0.00008355628],"domain_scores_gemma":[0.9980237,0.001499818,0.0001262264,0.0001240481,0.0001481166,0.00007811107],"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":[0.00007701105,0.00004251782,0.0003702221,0.00008275541,0.00002597462,0.00004758883,0.00003449262,0.9652105,0.0006108971,0.003910308,0.0003991608,0.02918852],"study_design_scores_gemma":[0.00001877381,0.00003074596,0.0000698994,0.000007469543,0.000006472173,0.00001062288,0.000008235332,0.9964276,0.0004049578,0.002808535,0.0002036152,0.000003077589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06963231,0.000646949,0.9253576,0.0001815699,0.00003156782,0.0001195893,0.0001041414,0.0007704362,0.003155855],"genre_scores_gemma":[0.687546,0.0003345082,0.3103176,0.0000973208,0.00002068804,0.0003616067,0.0002930916,0.00008854477,0.0009406133],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003862241,"threshold_uncertainty_score":0.007679522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.028466300042394,"score_gpt":0.2755053467250883,"score_spread":0.2470390466826942,"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."}}