{"id":"W2422344913","doi":"10.1109/syscon.2016.7490542","title":"A fuzzy reinforcement learning algorithm using a predictor for pursuit-evasion games","year":2016,"lang":"en","type":"article","venue":"","topic":"Guidance and Control Systems","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Pursuer; Pursuit-evasion; Computer science; Reinforcement learning; Kalman filter; Fuzzy logic; Artificial intelligence; Algorithm; Position (finance); Filter (signal processing); Control theory (sociology); Machine learning; Mathematical optimization; Mathematics; Computer vision; Control (management)","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":[0.001139124,0.0006677283,0.0009100131,0.0003764458,0.0004664333,0.0007333998,0.001043496,0.001001869,0.001769448],"category_scores_gemma":[0.002663377,0.0002520122,0.0003305133,0.0002465942,0.000745176,0.0006041042,0.0006224443,0.001093251,0.0002693949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008027477,"about_ca_system_score_gemma":0.001459245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008535658,"about_ca_topic_score_gemma":0.004326267,"domain_scores_codex":[0.9996146,0.0001060138,0.00002326199,0.00008624713,0.0001094673,0.00006041306],"domain_scores_gemma":[0.9990495,0.0005011193,0.0001053392,0.00003414924,0.0002461357,0.00006378008],"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":[0.0001427987,0.0001059439,0.00126136,0.00006362166,0.00004475938,0.000105947,0.0001180233,0.9095837,0.002549371,0.009170594,0.0007883551,0.07606551],"study_design_scores_gemma":[0.00001548761,0.00003916421,0.00005687858,0.000003458199,0.00000400645,0.0000102993,0.000003308407,0.9988523,0.0002838234,0.0005575593,0.0001700804,0.00000371937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03364046,0.0002461428,0.9621568,0.0001960171,0.00006600594,0.00008149131,0.00001598841,0.0004428715,0.003154302],"genre_scores_gemma":[0.9277765,0.000168752,0.0684533,0.0001096919,0.00002965515,0.0001556891,0.00003603115,0.00002019858,0.003250211],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008535658,"threshold_uncertainty_score":0.01697195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01458177833022805,"score_gpt":0.2218174049497801,"score_spread":0.207235626619552,"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."}}