{"id":"W2139042205","doi":"10.1609/socs.v2i1.18203","title":"Faster Optimal and Suboptimal Hierarchical Search","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency; National Science Foundation","keywords":"Heuristic; Incremental heuristic search; Weighting; Computer science; Beam search; Best-first search; Search algorithm; Simple (philosophy); Greedy algorithm; Local search (optimization); Function (biology); Bidirectional search; Mathematical optimization; Algorithm; Mathematics; Artificial intelligence","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":[0.0009431372,0.000154927,0.0001761264,0.00008789569,0.0002185567,0.000433458,0.001499478,0.00009743624,0.00002614937],"category_scores_gemma":[0.0001511407,0.00012523,0.00009587989,0.0003340617,0.0001572627,0.0003450215,0.001335677,0.0005496176,0.0000120579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008775607,"about_ca_system_score_gemma":0.0001422168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003094035,"about_ca_topic_score_gemma":3.05692e-7,"domain_scores_codex":[0.9977129,0.00004472802,0.000269559,0.0004713495,0.001174358,0.0003271532],"domain_scores_gemma":[0.9986273,0.0003043542,0.00007570872,0.0001770493,0.0006885774,0.0001269828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002083897,0.0002806793,0.01366923,0.00006897059,0.0001008547,0.00001307183,0.001385142,0.0005335468,0.07735037,0.9025163,0.001327636,0.002545814],"study_design_scores_gemma":[0.003888429,0.0008889686,0.009112708,0.0006452761,0.00003006157,0.0002810586,0.0002908374,0.1145298,0.834953,0.02312844,0.01142848,0.0008229068],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9557149,0.00004738472,0.00102593,0.02371405,0.00207943,0.0001855678,0.00001010719,0.00007071694,0.01715194],"genre_scores_gemma":[0.9949095,0.00001601981,0.003845866,0.0002049953,0.0002724831,0.000008532653,0.000002657147,0.0000135314,0.0007263893],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8793879,"threshold_uncertainty_score":0.5106732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0149486210134715,"score_gpt":0.250929431142466,"score_spread":0.2359808101289945,"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."}}