{"id":"W3006111153","doi":"10.22215/etd/2016-11500","title":"Enhancing AI-Based Game Playing Using Adaptive Data Structures","year":2016,"lang":"en","type":"dissertation","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Ranking (information retrieval); Sophistication; Pruning; Premise; Field (mathematics); Core (optical fiber); Tree (set theory); Game tree; Sequential game; Artificial intelligence; Game theory; 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":[0.002388534,0.0008258251,0.0005489581,0.0008078715,0.0005449441,0.001922692,0.001996673,0.000999585,0.003301702],"category_scores_gemma":[0.01338333,0.0003567062,0.0006579336,0.0005669632,0.001442992,0.003378134,0.002591612,0.001628078,0.000671578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000807644,"about_ca_system_score_gemma":0.001143197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001320948,"about_ca_topic_score_gemma":0.001888874,"domain_scores_codex":[0.9979827,0.0007849389,0.0001360058,0.0003223405,0.000601314,0.0001726535],"domain_scores_gemma":[0.9916339,0.006062585,0.0005746603,0.0008533808,0.0004740529,0.0004014734],"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":[0.001092117,0.001720838,0.007823448,0.0008481511,0.0002077081,0.0004121142,0.002632324,0.241573,0.06001849,0.2784913,0.004615751,0.4005648],"study_design_scores_gemma":[0.0001379847,0.0004033901,0.0009748592,0.00004148554,0.00004243603,0.0001794239,0.0002493328,0.9061494,0.01002682,0.07517913,0.006581597,0.00003408992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1537875,0.0002786684,0.8308026,0.0006988259,0.00006832001,0.0004203876,0.0001256056,0.001610084,0.01220812],"genre_scores_gemma":[0.6012887,0.0001921301,0.3943402,0.0002124522,0.00002973224,0.0003769739,0.0001771504,0.0001113261,0.003271342],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003301702,"threshold_uncertainty_score":0.01263195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09145587251845948,"score_gpt":0.3627629948782847,"score_spread":0.2713071223598252,"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."}}