{"id":"W2759836588","doi":"10.1609/icaps.v27i1.13825","title":"Sufficient Conditions for Node Expansion in Bidirectional Heuristic Search","year":2017,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Node (physics); Heuristics; Mathematical optimization; Class (philosophy); Bidirectional search; Heuristic; Incremental heuristic search; Search algorithm; Beam search; Computer science; Front (military); Focus (optics); Mathematics; Algorithm; Artificial intelligence; Engineering","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":[0.0004248448,0.0001039764,0.0001240576,0.0001358406,0.0003945531,0.0004525147,0.0009654836,0.00005936589,0.000003228135],"category_scores_gemma":[0.000336708,0.00008411024,0.00003892115,0.00006652839,0.00008268975,0.0002747735,0.0002049348,0.0001875471,0.000001655836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003288527,"about_ca_system_score_gemma":0.0000815609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003806394,"about_ca_topic_score_gemma":0.000001220498,"domain_scores_codex":[0.9990393,0.000006193514,0.0002188498,0.0002796119,0.0002924252,0.0001636498],"domain_scores_gemma":[0.9991783,0.00008229457,0.0001858062,0.0001241964,0.0003854309,0.0000439239],"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":[0.0001248944,0.0002293534,0.04462207,0.0001204437,0.00006259129,0.000003474612,0.002394382,0.02156722,0.1092537,0.8189411,0.0003789612,0.0023018],"study_design_scores_gemma":[0.0002813863,0.00003866146,0.02312423,0.0006087492,0.000003244263,0.000009397041,0.0001598521,0.9621583,0.007668556,0.0058434,0.000007857084,0.00009637242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9814525,0.00001778485,0.01265529,0.002873622,0.0003778855,0.0001247545,0.00001461084,0.0001260814,0.002357529],"genre_scores_gemma":[0.9915617,0.000008026323,0.008223398,0.00007746697,0.00002981124,0.00001976367,0.000002854121,0.000005271111,0.00007174898],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9405911,"threshold_uncertainty_score":0.4363609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08026908485261397,"score_gpt":0.3518153585259207,"score_spread":0.2715462736733067,"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."}}