{"id":"W2794468939","doi":"","title":"Rewards Structure in Games: Learning a Compact Representation for Action Space.","year":2017,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Computer science; Representation (politics); Space (punctuation); Action (physics); Theoretical computer science; Artificial intelligence; Human–computer interaction; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001111444,0.001054936,0.001297153,0.0008275001,0.0004627859,0.001042626,0.002412537,0.001507632,0.00428988],"category_scores_gemma":[0.008829126,0.000679805,0.0006407169,0.0006225956,0.0008703699,0.002607668,0.001923869,0.003109567,0.0008343303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001363688,"about_ca_system_score_gemma":0.001413547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007074161,"about_ca_topic_score_gemma":0.01182583,"domain_scores_codex":[0.9993824,0.0002652347,0.00004032162,0.0001411733,0.00008775185,0.00008317358],"domain_scores_gemma":[0.9973848,0.001670353,0.0002279159,0.0002433118,0.0002303529,0.000243213],"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":[0.0008834579,0.0005258774,0.005092463,0.0002189125,0.0001039771,0.0001296516,0.0003049391,0.6161088,0.002219962,0.06235898,0.01261175,0.2994412],"study_design_scores_gemma":[0.00003294733,0.00007389327,0.0002558716,0.00001967289,0.000006591282,0.000009353113,0.00001709724,0.9604856,0.0002665197,0.03841941,0.000406957,0.000006214796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0850513,0.0008028314,0.9079488,0.0008660224,0.0001139672,0.0001766472,0.001063363,0.001134373,0.002842804],"genre_scores_gemma":[0.8367663,0.0002793991,0.1555608,0.000236491,0.00006818247,0.0004120078,0.001617812,0.0001435488,0.004915434],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007074161,"threshold_uncertainty_score":0.01435101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2837771291093321,"score_gpt":0.4380129574277635,"score_spread":0.1542358283184314,"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."}}