{"id":"W2767141304","doi":"10.1109/cig.2017.8080452","title":"Improving hearthstone AI by learning high-level rollout policies and bucketing chance node events","year":2017,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Monte Carlo tree search; Entertainment; Adversary; Video game; State (computer science); Node (physics); Artificial intelligence; Game tree; Action (physics); Computer security; Sequential game; Multimedia; Game theory; Programming language; Monte Carlo method; Mathematical economics; Engineering; Visual arts","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":[],"consensus_categories":[],"category_scores_codex":[0.0004594673,0.0001766619,0.0002017709,0.00006287349,0.001131815,0.000661982,0.0009784786,0.00007448359,0.0000185604],"category_scores_gemma":[0.0004343415,0.0001650216,0.00003934283,0.00006088279,0.0001401786,0.00112111,0.0008710435,0.0002621933,0.00009463485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002802878,"about_ca_system_score_gemma":0.00003505293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0048242,"about_ca_topic_score_gemma":0.0001807707,"domain_scores_codex":[0.9984857,0.00005352778,0.0002546844,0.0004683452,0.0002735516,0.0004641602],"domain_scores_gemma":[0.9988151,0.0001046499,0.0002227253,0.0006509267,0.00008928747,0.0001173418],"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":[0.00001781318,0.00009304293,0.101869,0.00005074179,0.00003923731,0.00001415363,0.005103541,0.000561882,0.1314219,0.02670568,0.001191642,0.7329313],"study_design_scores_gemma":[0.0005014553,0.0002896999,0.09220776,0.0002103554,0.0000179524,0.00004434728,0.0007273174,0.4892272,0.4012046,0.008554903,0.00557844,0.001435963],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4850872,0.0001367195,0.5085815,0.00477534,0.0003414319,0.0001152943,0.000001925388,0.0001882313,0.000772446],"genre_scores_gemma":[0.9760949,0.00002905235,0.019165,0.000651523,0.0001064177,0.000008766596,6.635336e-7,0.00001436278,0.003929314],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7314953,"threshold_uncertainty_score":0.8705119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03426155760626678,"score_gpt":0.3060478462723761,"score_spread":0.2717862886661093,"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."}}