{"id":"W2965027281","doi":"10.24963/ijcai.2019/174","title":"Iterative Budgeted Exponential Search","year":2019,"lang":"en","type":"preprint","venue":"","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Compute Canada; University of Denver","keywords":"Iterative deepening depth-first search; Search tree; Omega; Overhead (engineering); Tree (set theory); Heuristic; Mathematical optimization; Graph; Exponential function; Search algorithm; Computer science; Upper and lower bounds; Limit (mathematics); Mathematics; Branch and bound; Combinatorics; Algorithm; Discrete mathematics; Beam search; Incremental heuristic search","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004994357,0.0002716319,0.000296562,0.0001874388,0.0001163692,0.0006549319,0.001581246,0.0002795263,0.0001762974],"category_scores_gemma":[0.00002088497,0.0002417579,0.000131704,0.000163709,0.00002740043,0.0002115258,0.00240883,0.0009331165,0.0006895809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005596496,"about_ca_system_score_gemma":0.0003691452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002092507,"about_ca_topic_score_gemma":0.000003245052,"domain_scores_codex":[0.9978785,0.0002105359,0.0002673737,0.0008146429,0.0004299544,0.0003990291],"domain_scores_gemma":[0.9983464,0.000195855,0.00009991885,0.001090182,0.0001583552,0.0001092368],"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":[0.0001682253,0.0006136826,0.01568373,0.002116677,0.001215577,0.0006070956,0.06099101,0.3942806,0.005681372,0.1738874,0.2224628,0.1222919],"study_design_scores_gemma":[0.0004103706,0.0001311333,0.00079871,0.0003688037,0.00001286861,0.00001291081,0.00004544119,0.9832536,0.005937075,0.003873819,0.004325289,0.0008299992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01054677,0.0002206982,0.9631862,0.001082423,0.001687545,0.0003344645,0.00001428675,0.0004470398,0.02248054],"genre_scores_gemma":[0.8628309,0.0000112727,0.126098,0.0005092756,0.0002351176,0.00002584813,0.00009774192,0.00002149827,0.01017042],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8522841,"threshold_uncertainty_score":0.98586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03086939578868878,"score_gpt":0.2753862092209154,"score_spread":0.2445168134322266,"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."}}