{"id":"W2472694993","doi":"10.1016/j.tcs.2016.06.027","title":"Conspiracy number search with relative sibling scores","year":2016,"lang":"en","type":"article","venue":"Theoretical Computer Science","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Monte Carlo tree search; Heuristic; Null-move heuristic; Champion; Computer science; Tree (set theory); Node (physics); Iterative deepening depth-first search; Search tree; Game tree; Search algorithm; Bidirectional search; Mathematics; Algorithm; Theoretical computer science; Best-first search; Beam search; Monte Carlo method; Artificial intelligence; Statistics; Combinatorics; Game theory; Sequential game","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":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001553952,0.0002169356,0.0002116398,0.0001960159,0.000496826,0.0005106791,0.002940771,0.00005421532,0.0001839493],"category_scores_gemma":[0.0002321402,0.000119913,0.00005275925,0.001685484,0.00837663,0.002205868,0.00122286,0.000210831,0.001069152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009568018,"about_ca_system_score_gemma":0.0002940226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008931934,"about_ca_topic_score_gemma":0.000001508025,"domain_scores_codex":[0.9966615,0.0001526726,0.000305989,0.0009557331,0.00109365,0.0008304998],"domain_scores_gemma":[0.9968775,0.001204159,0.00007100838,0.001004952,0.0004923282,0.0003500852],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001325232,0.00003187402,0.002012509,0.000002058341,0.000004549905,0.00002629363,0.0006243741,0.00006071634,0.0009138804,0.9236671,0.00002042688,0.0726229],"study_design_scores_gemma":[0.0002858754,0.0005692263,0.00180036,0.0002769871,0.000007151446,0.0001594271,0.00003002716,0.1986498,0.1237687,0.6736009,0.0002143411,0.000637327],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1088261,0.000008063701,0.8801633,0.002927438,0.0002700979,0.0001640094,9.197548e-7,0.0002563638,0.007383774],"genre_scores_gemma":[0.7948104,0.000003578588,0.2046505,0.0003761947,0.0000943008,0.000005887656,6.303689e-8,0.000009268907,0.00004981369],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6859843,"threshold_uncertainty_score":0.9997087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0292662082695179,"score_gpt":0.3119599208179767,"score_spread":0.2826937125484588,"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."}}