{"id":"W2104784127","doi":"","title":"A new algorithm for generating equilibria in massive zero-sum games","year":2007,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Nash equilibrium; Computer science; Measure (data warehouse); Range (aeronautics); Zero-sum game; Competition (biology); Abstraction; Zero (linguistics); Mathematical economics; Monopoly; Game theory; Combinatorial game theory; Turns, rounds and time-keeping systems in games; State (computer science); Sequential game; Theoretical computer science; Algorithm; Game mechanics; Artificial intelligence; Mathematics; Video game design; Economics","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":[0.0005706608,0.0001381872,0.0001605941,0.0001460615,0.00006163229,0.0001771337,0.0006812459,0.00007705174,0.00004359049],"category_scores_gemma":[0.0001207597,0.0001271596,0.00006835761,0.0003767553,0.00002895546,0.0005046303,0.0002075816,0.00009615813,0.00005895872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006437625,"about_ca_system_score_gemma":0.0001163631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002564219,"about_ca_topic_score_gemma":0.000249575,"domain_scores_codex":[0.9984665,0.00002065251,0.0004013535,0.0004083233,0.0001985926,0.0005045864],"domain_scores_gemma":[0.9990308,0.0003358289,0.0000799034,0.000347759,0.00007688582,0.0001288143],"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":[0.000002708229,0.00001685233,0.0002102295,0.00000220929,0.000004577945,0.00001335292,0.0007573574,0.0002345189,0.006786483,0.0320533,0.00283395,0.9570845],"study_design_scores_gemma":[0.0001073739,0.00007135908,0.0001195214,0.00001467706,0.000001593635,0.00000458073,0.0001740532,0.86222,0.1112457,0.02297002,0.002873201,0.0001978999],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008207214,0.00009419506,0.9878158,0.0006330328,0.0005418033,0.0002844933,8.008659e-7,0.0001495094,0.002273184],"genre_scores_gemma":[0.0473946,0.000002779014,0.9486708,0.000501789,0.0002726605,0.00001294102,0.000001016343,0.00001187042,0.003131534],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9568866,"threshold_uncertainty_score":0.5185417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03670057004729418,"score_gpt":0.3139890392549305,"score_spread":0.2772884692076363,"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."}}