{"id":"W2474732749","doi":"10.1007/978-3-662-49619-0_2","title":"On Achieving History-Based Move Ordering in Adversarial Board Games Using Adaptive Data Structures","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Heuristics; Heuristic; A priori and a posteriori; Context (archaeology); Pruning; Adversarial system; Tree (set theory); Search tree; Monte Carlo tree search; Ranking (information retrieval); Game tree; Theoretical computer science; Artificial intelligence; Sequential game; Algorithm; Game theory; Search algorithm; Mathematical economics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.003763516,0.00205382,0.002523489,0.001154632,0.001430116,0.002410234,0.003978627,0.002729414,0.008532547],"category_scores_gemma":[0.01993748,0.001156025,0.001378033,0.00149855,0.003457315,0.00705577,0.007470957,0.006020302,0.001251564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002024536,"about_ca_system_score_gemma":0.004210901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006094805,"about_ca_topic_score_gemma":0.008956098,"domain_scores_codex":[0.9967631,0.001046033,0.0001934361,0.0005899205,0.0007762602,0.0006312262],"domain_scores_gemma":[0.9796295,0.01543642,0.0008221554,0.002339718,0.0007493828,0.001022908],"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":[0.0006107785,0.0003627101,0.001046283,0.0001953738,0.00007586238,0.0001063927,0.0003767792,0.6631682,0.002685248,0.239325,0.00586076,0.08618665],"study_design_scores_gemma":[0.00005195383,0.00009856237,0.00009671391,0.00002293882,0.00001255086,0.00001745952,0.00004224473,0.8588226,0.0006068362,0.1397104,0.0005021892,0.0000154805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03151891,0.0001862054,0.9598373,0.0004853822,0.00008376032,0.0002023318,0.0001594435,0.0008704087,0.006656347],"genre_scores_gemma":[0.7041029,0.0005116984,0.2776406,0.0004428167,0.0001565867,0.0005788942,0.0009036808,0.0005096609,0.015153],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008532547,"threshold_uncertainty_score":0.02854425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06677026653193777,"score_gpt":0.2875299927013429,"score_spread":0.2207597261694052,"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."}}