{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001901708,0.0009083889,0.000978895,0.0008055653,0.001416343,0.002762195,0.001468386,0.001464348,0.02674276],"category_scores_gemma":[0.01061302,0.0005674108,0.0008314747,0.0006779643,0.002108985,0.005669564,0.004634058,0.002472617,0.002039358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009821632,"about_ca_system_score_gemma":0.001309732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001273386,"about_ca_topic_score_gemma":0.001853877,"domain_scores_codex":[0.9979266,0.00085935,0.0001164894,0.0003679089,0.0004085006,0.0003212014],"domain_scores_gemma":[0.9962219,0.002175008,0.0003344145,0.00052673,0.0003583101,0.0003836112],"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":[0.0003297802,0.0001457904,0.001000168,0.0001994403,0.00005139206,0.000296502,0.0004607346,0.08111911,0.002359854,0.8668713,0.005302866,0.04186302],"study_design_scores_gemma":[0.0001525983,0.0001772479,0.0002438096,0.00007477309,0.00003394366,0.0001904667,0.0002319301,0.2643719,0.002465985,0.7175747,0.01445323,0.0000293584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.210543,0.0004213614,0.6474137,0.001682105,0.0002735888,0.0003994319,0.0004710513,0.001691366,0.1371044],"genre_scores_gemma":[0.8153195,0.0001714719,0.1610081,0.0007295976,0.00004839423,0.0003430098,0.0002861891,0.0003490165,0.02174468],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02674276,"threshold_uncertainty_score":0.08946341,"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."}}