A Machine Consciousness Approach To Urban Traffic Control

dc.creatorParaense
dc.creatorALO; Raizer
dc.creatorK; Gudwin
dc.creatorRR
dc.date2016
dc.date2016-12-06T18:32:34Z
dc.date2016-12-06T18:32:34Z
dc.date.accessioned2018-03-29T02:05:10Z
dc.date.available2018-03-29T02:05:10Z
dc.identifier2212-6848
dc.identifierBiologically Inspired Cognitive Architectures. ELSEVIER SCIENCE BV, n. 15, p. 61 - 73.
dc.identifier2212-683X
dc.identifierWOS:000370914100007
dc.identifier10.1016/j.bica.2015.10.001
dc.identifierhttp://www-sciencedirect-com.ez88.periodicos.capes.gov.br/science/article/pii/S2212683X15000614
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/320576
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1311342
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionIn this work, we present a distributed cognitive architecture used to control the traffic in an urban network. This architecture relies on a machine consciousness approach - Global Work-space Theory - in order to use competition and broadcast, allowing a group of local traffic controllers to interact, resulting in a better group performance. The main idea is that the local controllers usually perform a purely reactive behavior, defining the times of red and green lights, according just to local information. These local controllers compete in order to define which of them is experiencing the most critical traffic situation. The controller in the worst condition gains access to the global workspace, further broadcasting its condition (and its location) to all other controllers, asking for their help in dealing with its situation. This call from the controller accessing the global workspace will cause an interference in the reactive local behavior, for those local controllers with some chance in helping the controller in a critical condition, by containing traffic in its direction. This group behavior, coordinated by the global workspace strategy, turns the once reactive behavior into a kind of deliberative one. We show that this strategy is capable of improving the overall mean travel time of vehicles flowing through the urban network. A consistent gain in performance with the "Artificial Consciousness" traffic signal controller during all simulation time, throughout different simulated scenarios, could be observed, ranging from around 13.8% to more than 21%. (C) 2015 Elsevier B.V. All rights reserved.
dc.description15
dc.description
dc.description61
dc.description73
dc.descriptionCAPES, brazilian research agency
dc.descriptionFAPESP, Sao Paulo state agency
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description
dc.description
dc.description
dc.languageEnglish
dc.publisherELSEVIER SCIENCE BV
dc.publisherAMSTERDAM
dc.relationBiologically Inspired Cognitive Architectures
dc.rightsfechado
dc.sourceWOS
dc.subjectGlobal Workspace Theory
dc.subjectTraffic Lights Control
dc.subjectMachine Consciousness
dc.subjectCodelets
dc.titleA Machine Consciousness Approach To Urban Traffic Control
dc.typeArtículos de revistas


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