{"id":"W4388711601","doi":"10.1126/sciadv.adg3256","title":"Student of Games: A unified learning algorithm for both perfect and imperfect information games","year":2023,"lang":"en","type":"article","venue":"Science Advances","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Google (Canada); Alberta Medical Association","funders":"","keywords":"Perfect information; Computer science; Imperfect; Combinatorial game theory; Turns, rounds and time-keeping systems in games; Game mechanics; Limit (mathematics); Complete information; Artificial intelligence; Sequential game; Game theory; Video game design; Theoretical computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00146332,0.0001240812,0.0001767022,0.0003863799,0.0003625815,0.00026945,0.0008947956,0.0000282615,0.000002809891],"category_scores_gemma":[0.0004685451,0.0001045285,0.0000455274,0.001634545,0.0005928113,0.004202332,0.0003087117,0.00009057048,0.00002169259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000369999,"about_ca_system_score_gemma":0.0001506021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001784732,"about_ca_topic_score_gemma":0.000006236976,"domain_scores_codex":[0.998373,0.00003524466,0.0003140557,0.000342125,0.0005361572,0.0003994727],"domain_scores_gemma":[0.9989361,0.000309271,0.0001899318,0.0002646906,0.000216856,0.00008315082],"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":[0.000003127355,0.0000110755,0.001766612,0.0000257532,0.000003633407,6.894072e-7,0.005623581,0.002055726,0.004113991,0.003503226,0.00002566571,0.9828669],"study_design_scores_gemma":[0.000300885,0.001222816,0.01282632,0.0001321271,0.00001181666,0.00001324772,0.01463112,0.7493549,0.1942224,0.008165507,0.0185963,0.0005225506],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4230432,0.0004943106,0.5742783,0.0002015265,0.0006303707,0.000490639,0.000005409464,0.0002992741,0.0005569703],"genre_scores_gemma":[0.9394066,0.0002758189,0.05999054,0.00006458007,0.00003863882,0.00006225092,0.000002067602,0.000005649642,0.0001538312],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9823444,"threshold_uncertainty_score":0.426255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01890333554880741,"score_gpt":0.3266653686939193,"score_spread":0.3077620331451119,"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."}}