{"id":"W4313120064","doi":"10.1609/socs.v1i1.18159","title":"Bootstrap Learning of Heuristic Functions","year":2010,"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":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristic; Bootstrapping (finance); Null-move heuristic; Incremental heuristic search; Consistent heuristic; Computer science; Process (computing); Mathematical optimization; Algorithm; Sequence (biology); Mathematics; Artificial intelligence; Beam search; Search algorithm; Econometrics","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":[0.0007310787,0.0001107952,0.0001362102,0.0001056621,0.00018612,0.0001153803,0.001729688,0.00008071832,0.00002786957],"category_scores_gemma":[0.0003216255,0.00008900623,0.0001049228,0.0003033831,0.00009272886,0.000222589,0.0003743083,0.0006806562,0.00001308987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003720343,"about_ca_system_score_gemma":0.00007437092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009420775,"about_ca_topic_score_gemma":7.921489e-7,"domain_scores_codex":[0.9984286,0.00001422706,0.000280183,0.0002511426,0.000836121,0.0001897513],"domain_scores_gemma":[0.9986767,0.0002524731,0.0002085726,0.0001460661,0.0006541627,0.00006200328],"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":[0.00007251387,0.0001853869,0.02655324,0.00004090863,0.00005158989,4.86188e-7,0.0004775336,0.0004710871,0.2774408,0.692557,0.001371369,0.0007780799],"study_design_scores_gemma":[0.002875333,0.001558655,0.01450977,0.0005686147,0.00004777746,0.0000537039,0.0002054714,0.07038131,0.8183884,0.05923177,0.03146483,0.0007143444],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9476995,0.000008526912,0.001011197,0.005048013,0.008328986,0.0002009918,0.000007035951,0.00009730184,0.03759846],"genre_scores_gemma":[0.9984167,0.000002625122,0.000568069,0.00002967674,0.0002683823,0.000007921284,0.000001540136,0.000009545138,0.0006955512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6333252,"threshold_uncertainty_score":0.3629569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01505893033952621,"score_gpt":0.2562940177736008,"score_spread":0.2412350874340746,"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."}}