{"id":"W4385767723","doi":"10.24963/ijcai.2023/539","title":"Choosing Well Your Opponents: How to Guide the Synthesis of Programmatic Strategies","year":2023,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Compute Canada","keywords":"Oracle; Computer science; Set (abstract data type); Iterated function; Artificial intelligence; Machine learning; Theoretical computer science; Algorithm; Programming language; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006104683,0.0001134027,0.000146847,0.0001227369,0.0001133091,0.0004865814,0.001420886,0.00003227326,0.00003046035],"category_scores_gemma":[0.0003888998,0.00007255054,0.0000684222,0.0009319853,0.0000779489,0.0004660863,0.0003654255,0.00006176574,0.00053498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002436972,"about_ca_system_score_gemma":0.00007448449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001850862,"about_ca_topic_score_gemma":0.00005232495,"domain_scores_codex":[0.998737,0.00007516175,0.0002718169,0.0002537361,0.0003490043,0.0003132159],"domain_scores_gemma":[0.9986682,0.0004126487,0.00008292749,0.0006627696,0.000105975,0.00006748264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000007408772,0.0001085563,0.0007852046,0.0001017855,0.00008140027,0.00002543795,0.009241961,0.005357688,0.01363595,0.2119042,0.02526198,0.7334885],"study_design_scores_gemma":[0.00005675201,0.000209026,0.003067428,0.0003143468,0.00003561325,0.000014378,0.04645398,0.3103024,0.5543823,0.04695061,0.0375175,0.0006956649],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1089089,0.00003444729,0.8317789,0.03370618,0.0005336974,0.0007386786,0.000001480446,0.0008552916,0.02344242],"genre_scores_gemma":[0.9610689,0.0000108413,0.03615309,0.0001694869,0.00004868878,0.00006730535,2.598436e-7,0.00001053029,0.00247094],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.85216,"threshold_uncertainty_score":0.6876264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07164105053924498,"score_gpt":0.3274977855141981,"score_spread":0.2558567349749531,"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."}}