{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002331934,0.0004899059,0.001383412,0.001355757,0.0007818543,0.001283947,0.002023549,0.001231268,0.01162183],"category_scores_gemma":[0.0282365,0.0002921433,0.0004337851,0.001402402,0.0009993879,0.004248791,0.00193906,0.0008667859,0.0009156674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008749135,"about_ca_system_score_gemma":0.001339518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00294934,"about_ca_topic_score_gemma":0.005080136,"domain_scores_codex":[0.9981639,0.0008048065,0.0000637329,0.0003009371,0.000396093,0.0002705159],"domain_scores_gemma":[0.9854177,0.01065845,0.0008197025,0.001723275,0.0006814919,0.00069931],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005539517,0.001625003,0.03431055,0.0005101064,0.000271243,0.0004521974,0.0005834951,0.2723821,0.006709572,0.2369855,0.01804224,0.4225886],"study_design_scores_gemma":[0.000151669,0.0004284664,0.002799106,0.00002528002,0.00005535741,0.0001556228,0.0001051927,0.9009611,0.0007986606,0.09362088,0.0008738407,0.00002490832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7669726,0.0005575208,0.1997366,0.0009137626,0.000122001,0.0001740451,0.0003504201,0.0009139783,0.03025911],"genre_scores_gemma":[0.9766568,0.00004634221,0.01871538,0.0000470887,0.00003064371,0.00003518482,0.0001336526,0.00004980478,0.0042852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01162183,"threshold_uncertainty_score":0.03887892,"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."}}