{"id":74824,"date":"2018-03-09T23:19:56","date_gmt":"2018-03-09T23:19:56","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/ensemble-case-based-learning-for-multi-agent-systems\/"},"modified":"2018-03-09T23:19:56","modified_gmt":"2018-03-09T23:19:56","slug":"ensemble-case-based-learning-for-multi-agent-systems","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/matematicas\/ensemble-case-based-learning-for-multi-agent-systems\/","title":{"rendered":"Ensemble case based learning for multi-agent systems"},"content":{"rendered":"<h2>Tesis doctoral de <strong> Santi Onta\u00f1\u00f3n Villar <\/strong><\/h2>\n<p>Esta monograf\u00eda presenta un marco de trabajo para el aprendizajeen un escenario de datos distribuidos y con control descentralizado.Hemos basado nuestro marco de trabajo en sistemas multi-agente (mas)para poder tener control descentralizado, y en razonamiento basado encasos (cbr), dado que su naturaleza de aprendizaje perezoso lo hacenadecuado para sistemas multi-agentes din\u00e1micos. adem\u00e1s,estamos interesados en agentes aut\u00f3nomos que funcionen como {emensembles}. un ensemble de agentes soluciona problemas de lasiguiente manera: cada agente individual soluciona el problema actualindividualmente y hace su predicci\u00f3n, entonces todas esaspredicciones se agregan para formar una predicci\u00f3n global.As\u00ed pues, en este trabajo estamos interesados en desarrollarestrategias de aprendizaje basadas en casos y en ensembles parasistemas multi-agente.Concretamente, presentaremos un marco de trabajo llamado razonamiento basado en casos multi-agente (mac), una aproximaci\u00f3nal cbr basada en agentes. Cada agente individual en un sistema \/mac\/es capaz de aprender y soluciar problemas individualmente utilizando cbr con su base de casos individuales. Adem\u00e1s, cada base de casos es propiedad de un agente individual, y cualquier informaci\u00f3n dicha base de casos ser\u00e1 revelada o compartida \u00fanicamente si la gente lo decide as\u00ed. Por tanto, este marco de trabajo preserva la privacidad de los datos y la autonom\u00eda de los agentes para revelar informaci\u00f3n. \u00e9sta tesis se centra en desarrollar estrategias para que agentes individuales con capacidad de aprender puedan incrementar su rendimiento tanto cuando trabajan individualmente como cuando trabjan como un ensemble. Adem\u00e1s, las decisiones en un sistema macs toma de manera  descentralizada, dado que cada agente tiene autonom\u00eda de decisi\u00f3n. por tanto, las t\u00e9cnicas desarrolladas en este marco de trabajo consiguen un incremento del rendimiento como resultado de decisiones individuales tomadas de manera descentr<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Ensemble case based learning for multi-agent systems<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Ensemble case based learning for multi-agent systems <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Santi Onta\u00f1\u00f3n Villar <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Aut\u00f3noma de barcelona<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 21\/06\/2005<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Direcci\u00f3n y tribunal<\/h3>\n<ul>\n<li><strong>Director de la tesis<\/strong>\n<ul>\n<li>Enric Plaza Cervera<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: ram\u00f3n L\u00f3pez de m\u00e1ntaras bad\u00eda <\/li>\n<li>elisabet Golobardes rib\u00e9 (vocal)<\/li>\n<li>daniel Borrajo mill\u00e1n (vocal)<\/li>\n<li>beatriz L\u00f3pez ib\u00e1\u00f1ez (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Santi Onta\u00f1\u00f3n Villar Esta monograf\u00eda presenta un marco de trabajo para el aprendizajeen un escenario de datos [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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