{"id":"W4313034700","doi":"10.1609/socs.v15i1.21758","title":"Optimal Search with Neural Networks: Challenges and Approaches","year":2022,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Israel Science Foundation; United States-Israel Binational Science Foundation; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Heuristics; Computer science; Machine learning; Artificial neural network; Heuristic; Artificial intelligence; Implementation; Classifier (UML); Beam search; Incremental heuristic search; Search algorithm; Algorithm","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":[0.001496069,0.0001690417,0.000196097,0.0001729041,0.0003939309,0.0002709402,0.002861581,0.00004211344,0.00003008093],"category_scores_gemma":[0.00007861282,0.0001268896,0.00006098502,0.0004876243,0.000181523,0.0003363084,0.002805365,0.0006445181,0.00000148602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001702606,"about_ca_system_score_gemma":0.00007351946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002521976,"about_ca_topic_score_gemma":3.290594e-7,"domain_scores_codex":[0.9963713,0.00008587979,0.0002592095,0.0005438263,0.00238391,0.0003558301],"domain_scores_gemma":[0.9987809,0.0002298237,0.0001174082,0.0002298689,0.0005186058,0.0001233806],"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":[0.0004876068,0.0005593632,0.0009175203,0.00005491493,0.0001502429,0.000005333641,0.001687736,0.1282885,0.0005397946,0.856572,0.0003233107,0.01041365],"study_design_scores_gemma":[0.001171419,0.0007894121,0.0009090505,0.00001937045,0.000006744863,0.00006107672,0.0003955908,0.9921149,0.002642474,0.000924454,0.000777774,0.000187721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8229221,0.001025433,0.009628237,0.1037064,0.008347123,0.003267087,0.00003685459,0.0003760376,0.0506908],"genre_scores_gemma":[0.9962807,0.0001140805,0.002892153,0.00006294272,0.000223964,0.00009491174,0.000002052974,0.00002217846,0.000306986],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8638265,"threshold_uncertainty_score":0.5317578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04092795038074364,"score_gpt":0.2525866672370791,"score_spread":0.2116587168563355,"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."}}