{"id":"W2947517210","doi":"10.48550/arxiv.1905.11346","title":"Error Analysis and Correction for Weighted A*'s Suboptimality (Extended Version)","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Heuristics; Variety (cybernetics); Upper and lower bounds; Mathematical optimization; Scale (ratio); Mathematics; Branch and bound; Computer science; Algorithm; Statistics; Mathematical analysis; Physics","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":[0.005544976,0.001044141,0.0009728423,0.00134533,0.001046595,0.001768171,0.002175053,0.001430325,0.009467845],"category_scores_gemma":[0.04992967,0.0005465215,0.00116321,0.002198757,0.001676949,0.002696799,0.002759544,0.003124009,0.001355027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001617363,"about_ca_system_score_gemma":0.003884032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009339093,"about_ca_topic_score_gemma":0.008223313,"domain_scores_codex":[0.9938002,0.002171444,0.0004285728,0.00104023,0.002124976,0.0004344579],"domain_scores_gemma":[0.9644342,0.02133742,0.001760475,0.006715697,0.005389957,0.0003621416],"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":[0.0004031352,0.0002203201,0.00355894,0.0005132609,0.0001480265,0.0002880346,0.0004533833,0.5261404,0.007205243,0.1331039,0.01579382,0.3121715],"study_design_scores_gemma":[0.00004320311,0.0001073852,0.0006712979,0.00006592018,0.00003133737,0.000133964,0.00008634258,0.9148779,0.005287231,0.07219587,0.006468853,0.00003068387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02183297,0.0004063222,0.96831,0.0007529756,0.0002603911,0.0001309521,0.0002152165,0.001079155,0.00701216],"genre_scores_gemma":[0.2971715,0.0002828991,0.6950371,0.0003929613,0.00009833819,0.000360089,0.000396891,0.0005720313,0.005688212],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009467845,"threshold_uncertainty_score":0.03167307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04793379003341202,"score_gpt":0.2005591049034761,"score_spread":0.152625314870064,"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."}}