{"id":"W1977849109","doi":"10.1109/syscon.2014.6819239","title":"FPGA implementation of multiple Pursuit-Evasion games with decentralized Learning Automata","year":2014,"lang":"en","type":"article","venue":"","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Field-programmable gate array; Computer science; Learning automata; VHDL; Hardware description language; Markov chain; Reconfigurable computing; Embedded system; Automaton; Artificial intelligence; Machine learning","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":[0.0003697724,0.0000720961,0.0001032837,0.00007579956,0.00007520543,0.0000948988,0.0003268035,0.00002130594,0.0001495964],"category_scores_gemma":[0.0000438375,0.00005349412,0.00002253879,0.000233021,0.00002343932,0.0003773251,0.000106244,0.00005600919,0.00001546512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001358094,"about_ca_system_score_gemma":0.00004034896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001080545,"about_ca_topic_score_gemma":0.00006660703,"domain_scores_codex":[0.9990163,0.0001160964,0.0001727617,0.0001915208,0.000314825,0.0001884632],"domain_scores_gemma":[0.9994246,0.00008568607,0.00009226799,0.0002228304,0.0001070542,0.00006754691],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004229507,0.0001757892,0.06695094,0.00008278515,0.00004882503,0.0000017563,0.00328762,0.04405387,0.007939211,0.05316057,0.001873522,0.8223828],"study_design_scores_gemma":[0.002407389,0.0003708391,0.01573827,0.00002150182,0.00000410182,0.000003367898,0.0002521633,0.9431377,0.02894504,0.000225477,0.008748996,0.0001451501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01912906,0.000006415052,0.9785461,0.0006540366,0.00003536757,0.0001890603,3.850858e-7,0.0001945017,0.001245071],"genre_scores_gemma":[0.8414046,0.00002349291,0.158178,0.0000941726,0.000005537119,0.000008509323,0.000009105566,0.000005627415,0.0002709694],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8990839,"threshold_uncertainty_score":0.2181427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01575181012944821,"score_gpt":0.2814753408664006,"score_spread":0.2657235307369524,"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."}}