Examinando por Materia "JUEGOS"

Mostrando1 - 4 de 4
  • Proyecto final de Grado
    ¿Una cuestión de suerte?: diseño de aplicaciones exitosas en Facebook
    (2010) Morkin, Miguel Ángel; Rodríguez Varela, Juan Pablo; Agres, Andrés
    "Este proyecto final trata sobre el diseño de aplicaciones exitosas dentro de Facebook a partir de su capacidad de propagación y reúso, haciendo foco en la industria de juegos y afines. Se parte de un análisis del estado actual de las aplicaciones en Facebook, abordando el segmento dominante de los juegos. A partir del entendimiento de los modelos de negocios, se desarrollan indicadores que permiten medir y seleccionar los ejemplos más exitosos. Las aplicaciones seleccionadas son analizadas como caso de estudio, y a partir de ellas se extraen conclusiones respecto a las estrategias a adoptar en el diseño. Finalmente, las conclusiones generadas son aplicadas integralmente sobre un desarrollo real."
  • Proyecto final de Grado
    Ethereum’s scability solutions for gaming
    (2019-12-05) Balaguer, Pedro; Caracciolo López, Juan Franco; Garrigó, Mariano; Cortesi, Mariano
    "Decentralized gaming is one of the newest blockchain related developments being researched nowadays since scalability solutions are required due to the inherit confirmation time and cost of blockchains. This project explores two of these and implements them in a proof-of-concept game. The first scalability solution, Plasma, allows players to freely send in-game assets to one another. An additional feature is proposed in which players are also able to trade these assets atomically. The second scalability solution, Force Move Channels, allows users to do off-chain computation in a PVP environment while relying in the blockchain for discrepancies. While these two solutions have their intrinsic limitations, a working prototype is proposed where users can purchase in-game assets and battle with them using minimal costs compared to previous Ethereum’s implementations."
  • Capítulo de libro
    Games and gamification in the classroom
    (2020) Gómez, Silvia Alicia
    "The power of seduction generated by video games in the new generations makes its use in education promising, which helps to achieve a highly motivated group of students and obtain a more efficient learning. This is how the Serious Games and the Gamification arise. The first ones are interactive software specially designed to favor the acquisition of knowledge and skills or behavior changes, in an environment similar to videogames. The second one just applies the elements and mechanisms that make videogames captivating, although used in non-game contexts. The idea behind this consists in offering students playful/fun motivational experiences and transforming the learning process into a much more attractive one. The experiences already conducted with both proposals provide results that invite us to continue moving forward on that path."
  • Artículo de Publicación Periódica
    Training a gaming agent on brainwaves
    (2020-12-07) Bartolomé, Francisco; Moreno, Juan; Navas, Natalia; Vitali, José; Ramele, Rodrigo; Santos, Juan Miguel
    "Error-related potential (ErrP) are a particular type of Event-Related Potential (ERP) elicited by a person attending a recognizable error. These Electroencephalographic (EEG) signals can be used to train a gaming agent by a Reinforcement Learning (RL) algorithm to learn an optimal policy. The experimental process consists of an observational human critic (OHC) observing a simple game scenario while their brain signals are captured. The game consists of a grid, where a blue spot has to reach a desired target in the fewest amount of steps. Results show that there is an effective transfer of information and that the agent successfully learns to solve the game efficiently, from the initial 97 steps on average required to reach the target to the optimal number of 8 steps. Our results are expressed in threefold: (i) the mechanics of a simple grid-based game that can elicit the ErrP signal component, (ii) the verification that the reward function only penalizes wrong steps, which means that type II error (not properly identifying a wrong movement) does not affect significantly the agent learning process; (iii) collaborative rewards from multiple observational human critics can be used to train the algorithm effectively and can compensate low classification accuracies and a limited scope of transfer learning schemes."