{"id":"W4382240209","doi":"10.1609/aaai.v37i4.25626","title":"Show Me the Way! Bilevel Search for Synthesizing Programmatic Strategies","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Canadian Institute for Advanced Research","keywords":"Computer science; Space (punctuation); Function (biology); Search algorithm; Set (abstract data type); Differentiable function; Bilevel optimization; Feature (linguistics); Evaluation function; Theoretical computer science; Algorithm; Artificial intelligence; Mathematics; Optimization problem; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001263706,0.001053555,0.00070924,0.0007160334,0.0006080582,0.001412172,0.0009265344,0.001131493,0.005822278],"category_scores_gemma":[0.006119442,0.0005080454,0.0008091065,0.0004737389,0.001564655,0.001597091,0.001553391,0.001535591,0.0009438643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008648253,"about_ca_system_score_gemma":0.001223374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002085499,"about_ca_topic_score_gemma":0.002713461,"domain_scores_codex":[0.9994047,0.0002042672,0.00004431558,0.0001253195,0.0001482447,0.00007318328],"domain_scores_gemma":[0.9989783,0.0006686733,0.00008086608,0.00009089468,0.0001153665,0.00006598472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002300412,0.0001383472,0.002179335,0.000406796,0.0000854595,0.0002296235,0.0004911397,0.6393704,0.008331365,0.1628347,0.003162985,0.1825399],"study_design_scores_gemma":[0.00004618828,0.0001048852,0.0001001297,0.00006010371,0.00002263997,0.00006100156,0.00008442584,0.9318534,0.002399098,0.05964257,0.005610514,0.0000150427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0393364,0.000502736,0.9489499,0.0004582443,0.00006829633,0.0001147526,0.00007647236,0.001165371,0.009327856],"genre_scores_gemma":[0.4112104,0.0004000472,0.5813571,0.000257447,0.00002449845,0.00043974,0.0001788845,0.0003960303,0.005735881],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005822278,"threshold_uncertainty_score":0.01947743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1857926898496378,"score_gpt":0.3534989015197543,"score_spread":0.1677062116701165,"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."}}