{"id":"W2326795372","doi":"10.1109/icma.2014.6885793","title":"A two stage learning technique using PSO-based FLC and QFIS for the pursuit evasion differential game","year":2014,"lang":"en","type":"article","venue":"","topic":"Guidance and Control Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Pursuit-evasion; Pursuer; Fuzzy logic; Computer science; Particle swarm optimization; Artificial intelligence; Mathematical optimization; Control theory (sociology); Algorithm; Control (management); Mathematics","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":[0.001256803,0.0007076727,0.0009598219,0.0006051343,0.0004905578,0.0006440327,0.001546872,0.001208901,0.001505253],"category_scores_gemma":[0.002230495,0.0003097054,0.0006774947,0.0004174415,0.000911421,0.000684982,0.001039717,0.001053763,0.0001923393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00087939,"about_ca_system_score_gemma":0.0009700828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005891426,"about_ca_topic_score_gemma":0.004194608,"domain_scores_codex":[0.9993444,0.0001676558,0.00003784418,0.000114616,0.000270385,0.00006504085],"domain_scores_gemma":[0.9993092,0.0003621939,0.00007525131,0.0000445031,0.0001684587,0.00004037666],"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":[0.0000946732,0.0001486546,0.001016353,0.0001305423,0.00008102664,0.0001812422,0.0001551972,0.8278251,0.006139998,0.02012069,0.000947441,0.1431592],"study_design_scores_gemma":[0.00001201681,0.00005977487,0.00007738453,0.000003470579,0.000005982451,0.00002316211,0.000003155984,0.9982333,0.0003980754,0.0008897103,0.0002887073,0.000005183793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009184453,0.0001304669,0.9882019,0.00008720309,0.00003758926,0.000069505,0.00000573032,0.000130067,0.002153048],"genre_scores_gemma":[0.7943839,0.0001645295,0.2016148,0.0001720961,0.00004949366,0.0002563967,0.00002976502,0.00002356692,0.003305464],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005891426,"threshold_uncertainty_score":0.01171422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01287345478823287,"score_gpt":0.2338324395680689,"score_spread":0.220958984779836,"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."}}