{"id":"W4391307866","doi":"10.1109/smc53992.2023.10394149","title":"Learning a Policy for Pursuit-Evasion Games Using Spiking Neural Networks and the STDP Algorithm","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Pursuer; Pursuit-evasion; Spiking neural network; Computer science; Point (geometry); Artificial intelligence; Evasion (ethics); Spike (software development); Artificial neural network; Control (management); Game theory; Algorithm; Mathematical optimization; Mathematics; Mathematical economics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002708879,0.000126373,0.0001486942,0.00007895933,0.0003064205,0.00006743203,0.00008598548,0.00004168331,0.000002073645],"category_scores_gemma":[0.00008401853,0.00009084577,0.00005510738,0.0003174318,0.00003985098,0.000124323,0.00008892499,0.0002113961,0.00000101997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002098669,"about_ca_system_score_gemma":0.000004151162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009004794,"about_ca_topic_score_gemma":0.000001490643,"domain_scores_codex":[0.9992695,0.00002906639,0.0001411463,0.000145397,0.00007658242,0.0003382732],"domain_scores_gemma":[0.999453,0.0003776174,0.00002641414,0.00008436985,0.00001559095,0.000042997],"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":[0.000008543201,5.846137e-7,0.00002081821,0.00001596878,0.000007046104,0.000002200509,0.0001239768,0.7953472,0.001210818,0.0002181399,0.00002007863,0.2030246],"study_design_scores_gemma":[0.0005403979,0.00002301144,0.00009169328,0.00002472483,0.00001010628,0.0000192611,0.0002298749,0.9972617,0.0007980347,0.0003818615,0.0005023633,0.0001169815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5123892,0.0003383433,0.4854196,0.0002072418,0.0003338706,0.0003091127,7.585718e-7,0.0008283372,0.0001734377],"genre_scores_gemma":[0.9955656,0.0000908584,0.003315029,0.0001091462,0.000654539,0.00001104865,0.000003599062,0.00003981789,0.0002103863],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4831763,"threshold_uncertainty_score":0.3704583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0238739348499211,"score_gpt":0.2774436207510811,"score_spread":0.25356968590116,"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."}}