Examinando por Autor "Altamiranda, Junior"

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    Ítem
    Adaptive System for the Generation of Emerging Behaviors in Serious Emerging Games
    (Instituto Politecnico Nacional, 2020-01-01) Aguilar, Jose; Altamiranda, Junior; Diaz, Francisco; Universidad EAFIT. Escuela de Ciencias; Modelado Matemático
    A video game adaptation system (SAV) for serious emergent games (JSE), allows emergent behaviors in the game, such as the appearance of environments, events, narratives and characters, among others, in order to adapt to the context in the one that is developing. In previous articles the architecture of a JSE engine has been proposed. Furthermore, a first subsystem has been proposed that allows the emergence of a JSE according to the objectives of the environment, based on the ant colony optimization algorithm (ACO). In the present work, the second component of said architecture is specified, the SAV, which allows its dynamic adaptation (during the JSE). The SAV is made up of the sub-layers of strategies, sequences and properties, which manage each of these types of possible emergencies in a JSE, with the intention of dynamically adapting it to the context-domain where the game is being played. Furthermore, in this work the behavior of these sublayers is analyzed in a specific case study, showing very encouraging results of SAV in the educational context of an intelligent classroom (SaCI).
  • No hay miniatura disponible
    Ítem
    Adaptive System for the Generation of Emerging Behaviors in Serious Emerging Games
    (Instituto Politecnico Nacional, 2020-01-01) Aguilar, Jose; Altamiranda, Junior; Diaz, Francisco; Aguilar, Jose; Altamiranda, Junior; Diaz, Francisco; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las Comunicaciones
    A video game adaptation system (SAV) for serious emergent games (JSE), allows emergent behaviors in the game, such as the appearance of environments, events, narratives and characters, among others, in order to adapt to the context in the one that is developing. In previous articles the architecture of a JSE engine has been proposed. Furthermore, a first subsystem has been proposed that allows the emergence of a JSE according to the objectives of the environment, based on the ant colony optimization algorithm (ACO). In the present work, the second component of said architecture is specified, the SAV, which allows its dynamic adaptation (during the JSE). The SAV is made up of the sub-layers of strategies, sequences and properties, which manage each of these types of possible emergencies in a JSE, with the intention of dynamically adapting it to the context-domain where the game is being played. Furthermore, in this work the behavior of these sublayers is analyzed in a specific case study, showing very encouraging results of SAV in the educational context of an intelligent classroom (SaCI).