{"id":"W2294431252","doi":"10.1007/978-3-319-24069-5_21","title":"Enhancing History-Based Move Ordering in Game Playing Using Adaptive Data Structures","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Heuristics; Heuristic; Variety (cybernetics); Field (mathematics); Ranking (information retrieval); Adversary; Perspective (graphical); Combinatorial game theory; Video game design; Artificial intelligence; Sequential game; Game theory; Game mechanics; Mathematical economics; Computer security; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.001729123,0.000720973,0.0007615117,0.00144026,0.0001630618,0.0004063501,0.007494383,0.0004136379,0.00003444631],"category_scores_gemma":[0.0004625375,0.0007310404,0.00008451202,0.0007123366,0.000906113,0.001554355,0.0036225,0.001328543,0.00002017895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002678618,"about_ca_system_score_gemma":0.002923112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005531819,"about_ca_topic_score_gemma":0.001420019,"domain_scores_codex":[0.9940305,0.00008243893,0.000922186,0.002521083,0.001453688,0.0009900784],"domain_scores_gemma":[0.995265,0.0007064943,0.0005281878,0.00290881,0.0003606479,0.000230865],"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":[0.00001874353,0.00002415502,0.0001027439,0.0000419981,0.00001099711,0.0002692265,0.002902783,0.7425273,0.001397883,0.008842007,0.00001851349,0.2438436],"study_design_scores_gemma":[0.000113087,0.00007204571,0.00002822872,0.0006021808,0.00000678356,0.00002950503,0.000001681791,0.9255974,0.002884818,0.06924251,0.000667307,0.0007544085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006346722,0.001661361,0.9933326,0.0001134983,0.002927501,0.0004267114,0.000009454589,0.0001596274,0.0007346342],"genre_scores_gemma":[0.3302969,0.000008010351,0.6685585,0.0006187509,0.0004059311,0.00000414673,0.000007250616,0.0000548381,0.00004566452],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3296623,"threshold_uncertainty_score":0.999514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1214495866719911,"score_gpt":0.3116613849646072,"score_spread":0.1902117982926161,"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."}}