{"id":"W4283791892","doi":"10.1609/aaai.v36i7.20744","title":"What Can We Learn Even from the Weakest? Learning Sketches for Programmatic Strategies","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Compute Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Canadian Institute for Advanced Research","keywords":"Sketch; Computer science; Cloning (programming); Action (physics); Artificial intelligence; Human–computer interaction; Programming language; Algorithm","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":[0.002254588,0.001177202,0.0007757092,0.0005704217,0.0004310174,0.001570262,0.001305906,0.001197,0.005053977],"category_scores_gemma":[0.02090735,0.0007410569,0.0009975643,0.000228065,0.002086394,0.006101887,0.001941701,0.002403769,0.001193212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005996673,"about_ca_system_score_gemma":0.0006078436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006625083,"about_ca_topic_score_gemma":0.001160288,"domain_scores_codex":[0.9989826,0.0003545347,0.00006983565,0.0002695779,0.0002440444,0.00007940381],"domain_scores_gemma":[0.9941698,0.004001196,0.0002699703,0.001019007,0.0003162681,0.0002237061],"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":[0.0004193398,0.0002221417,0.006975267,0.00157547,0.0003370419,0.000348631,0.00131012,0.233986,0.02043566,0.2562788,0.005983236,0.4721283],"study_design_scores_gemma":[0.0001153981,0.0004464552,0.0008713852,0.0003341146,0.0001359297,0.0003029261,0.0003465516,0.5277596,0.01514341,0.4305919,0.02386522,0.00008705915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08307689,0.001940769,0.9017907,0.00257358,0.000200412,0.0001447313,0.0002185094,0.001739825,0.008314485],"genre_scores_gemma":[0.5618934,0.001482705,0.4277813,0.0006672893,0.00009467371,0.000254089,0.0003999731,0.0003810983,0.007045584],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005053977,"threshold_uncertainty_score":0.01690722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09476838440004962,"score_gpt":0.3105838047822173,"score_spread":0.2158154203821676,"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."}}