{"id":"W4404916257","doi":"10.1109/icons62911.2024.00020","title":"Timing Actions in Games Through Bio-Inspired Reinforcement Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reinforcement learning; Computer science; Artificial intelligence; Error-driven learning; Reinforcement; Human–computer interaction; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008772923,0.00006343273,0.00005337569,0.00008165714,0.0001302401,0.0001256192,0.0002042748,0.00002579231,0.00007681252],"category_scores_gemma":[0.000007078416,0.00005691238,0.00003399099,0.000583351,0.00001708658,0.0006080158,0.0001029698,0.0001299662,0.0002035751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006115314,"about_ca_system_score_gemma":0.0000495606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001127483,"about_ca_topic_score_gemma":0.00001136717,"domain_scores_codex":[0.9993586,0.00001228296,0.0001420131,0.0002296787,0.0001082113,0.0001491931],"domain_scores_gemma":[0.9997168,0.0000517981,0.00001407756,0.0001765265,0.00001555363,0.00002528588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[3.481006e-7,0.00002893721,0.0001136312,0.0000118802,0.000009850274,0.000007378446,0.001082669,0.03146824,0.0006931568,0.9269188,0.002499204,0.03716593],"study_design_scores_gemma":[0.00005163105,0.00001902584,0.0006145061,0.00002864993,0.000001705062,0.000007869105,0.000110601,0.827247,0.0004491877,0.004227783,0.167153,0.00008906241],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00147686,0.0002198834,0.9597358,0.004350784,0.00014508,0.00008989152,2.15442e-7,0.0004000608,0.03358141],"genre_scores_gemma":[0.9035675,0.00009461122,0.08603221,0.0001633669,0.0000646667,0.00006854914,0.000005011171,0.000005736709,0.009998333],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.922691,"threshold_uncertainty_score":0.2616614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04102234093842048,"score_gpt":0.301517680026176,"score_spread":0.2604953390877555,"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."}}