{"id":"W2047316657","doi":"10.1109/tciaig.2014.2345398","title":"Stronger Virtual Connections in Hex","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Computational Intelligence and AI in Games","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Monte Carlo tree search; Bottleneck; Computer science; Connection (principal bundle); Set (abstract data type); Solver; Theoretical computer science; Search algorithm; Search tree; Tree (set theory); Iterative deepening depth-first search; Computational complexity theory; Algorithm; Monte Carlo method; Beam search; Mathematics; Best-first search; Programming language; Combinatorics","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":[0.0003524663,0.0002069332,0.0002239736,0.0005640726,0.0001421711,0.0001583468,0.000412809,0.00009821777,0.00005643498],"category_scores_gemma":[0.00003036453,0.0002149613,0.00006805923,0.0007200312,0.0001896931,0.0006179985,0.000007257342,0.0004187303,0.000121425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007667907,"about_ca_system_score_gemma":0.00007287894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001612157,"about_ca_topic_score_gemma":0.0004716636,"domain_scores_codex":[0.9982022,0.0001395508,0.0005080007,0.0005329653,0.0002986181,0.0003186705],"domain_scores_gemma":[0.9985455,0.0009463077,0.00006325973,0.0002468986,0.0001023804,0.00009566843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001136334,0.0001689208,0.0001556307,0.000003921481,0.000006712394,0.000003835697,0.0007599668,0.7090471,0.00004293295,0.0499619,0.00002617531,0.2398115],"study_design_scores_gemma":[0.00009512098,0.000255696,0.001369902,0.00007628631,0.000003378598,0.00001712622,0.0004218312,0.9267939,0.007557443,0.06269336,0.0004403829,0.0002756077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04191322,0.0000353026,0.9547783,0.002154344,0.000551024,0.0001673638,0.000003791672,0.00009026899,0.0003063621],"genre_scores_gemma":[0.9901772,0.00004712438,0.008610502,0.0009154212,0.00003729675,0.00005148449,0.000001318606,0.00001144254,0.0001482588],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9482639,"threshold_uncertainty_score":0.8765867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02751037634781225,"score_gpt":0.2917056138452298,"score_spread":0.2641952374974176,"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."}}