{"id":"W2193259386","doi":"10.1609/aaai.v26i1.8401","title":"Using Sliding Windows to Generate Action Abstractions in Extensive-Form Games","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta; Compute Canada","keywords":"Abstraction; Limit (mathematics); Computer science; Action (physics); Flexibility (engineering); A priori and a posteriori; Range (aeronautics); Value (mathematics); Theoretical computer science; Algorithm; Mathematics; Machine learning; Statistics","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":[0.002163039,0.001124958,0.001110859,0.0007758533,0.0005210745,0.00104156,0.001199537,0.0008316954,0.003607761],"category_scores_gemma":[0.008657959,0.0006921758,0.0008619452,0.0004373717,0.001699658,0.002556067,0.002267411,0.001761087,0.0004607045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001023544,"about_ca_system_score_gemma":0.0009157312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001678,"about_ca_topic_score_gemma":0.002430588,"domain_scores_codex":[0.9990807,0.0003233892,0.0000549647,0.0001912607,0.0002426667,0.0001068984],"domain_scores_gemma":[0.9968932,0.00233381,0.0001873184,0.000296703,0.0001321876,0.0001568423],"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":[0.0002923915,0.0001461569,0.001396354,0.0001221313,0.00004873573,0.0001782091,0.0005322822,0.8168553,0.006641558,0.07942764,0.0008608547,0.09349836],"study_design_scores_gemma":[0.00003132,0.00007357264,0.0001264114,0.00001477351,0.000007875375,0.00001523593,0.00003260736,0.9548861,0.001640197,0.04240975,0.0007530868,0.000009048747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06489643,0.0001142267,0.9309908,0.0001109861,0.00002861969,0.0001558217,0.00006468678,0.0006914877,0.002946861],"genre_scores_gemma":[0.6873158,0.0001245394,0.3090308,0.00008399576,0.00001765263,0.0004184148,0.0001706898,0.000199909,0.00263821],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003607761,"threshold_uncertainty_score":0.01206911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2221330789194287,"score_gpt":0.3698435559604664,"score_spread":0.1477104770410377,"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."}}