4 research outputs found

    A.T.L.A.S.: Automatic Terrain and Labels Assembling Software

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    The interactivity and the decision making processes typica lof a video game have a strong influence on how the story of the game should be told, but also on how the imaginary world of the game, where the story takes place, should be structured. As a consequence, there is a growing interest in the development of tools able to couple well with the increasing demanding peculiarities of \u201cgame writing\u201d and\u201cworld building\u201d activities, especially when game or level designers are called to do also the work of a writer. In this paper, we present A.T.L.A.S.(Automatic Terrain and Labels Assembling Software), a tool aimed at the automatic creation of complex imaginary worlds for video games, based on Procedural Content Generation techniques, but characterized also by a story-driven approach

    Exploring player experience and social networks in MOBA Games: The case of League of Legends

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    A pesar de la popularidad de los juegos de arena de combate multijugador en línea (MOBA en inglés) como League of Legends (LoL), tanto la experiencia de jugador (PE) que proporciona este género relativamente reciente como las redes sociales que se generan a su alrededor siguen, en gran medida, inexplorados. Con el incremento del tiempo que los jugadores dedican a este tipo de juegos competitivos en línea, los impactos positivos y negativos de hacerlo cobran relevancia; es, por lo tanto, importante entender cómo se estructura dicha experiencia para abordar de forma sistemática los mecanismos que desencadenan respuestas de los jugadores. El presente trabajo empieza obteniendo y caracterizando una muestra de jugadores de League of Legends y sigue con el uso de las variables resultantes y de la estructura de las relaciones sociales como entradas para explorar su relación con la experiencia de los jugadores. Al fin y al cabo, la PE es básica para involucrar al jugador y, por lo tanto, es clave para el éxito de cualquier juego digital. Los resultados muestran, entre otros, cómo los jugadores de League of Legends perciben el juego como “justo” para su nivel de competencia en cualquier rango, mientras que su afinidad respecto a los compañeros se ve afectada por la estructura social. La empatía y los sentimientos negativos, no obstante, no parecen verse afectados por la composición del equipo. Entender la experiencia del jugador en League of Legends puede no tan sólo ser útil para mejorar el propio LoL o los juegos de tipo MOBA, sino también para desarrollar juegos más inmersivos a la vez que se mejora su calidad. A medida que los juegos competitivos online se convierten rápidamente en una de las mayores actividades colectivas humanas a nivel global, la investigación sobre la experiencia del jugador adquiere también una importancia crucial

    EMOTIONS RECOGNITION IN VIDEO GAME PLAYERS USING PHYSIOLOGICAL INFORMATION

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    Video games are interactive software able to arouse different kinds of emotions in players. Usually, the game designer tries to define a set of game features able to enjoy, engage, and/or educate the consumers. Through the gameplay, the narrative, and the game environment, a video game is able to interact with players' intellect and emotions. Thanks to the technological developments of the last years, the gaming industry has grown to become one of the most important entertainment markets. The scientific community and private companies have put a lot of efforts on the technical aspects as well as on the interaction aspects between the players and the video game. Considering the game design, many theories have been proposed to define some guidelines to design games able to arouse specific emotions in consumers. They mainly use interviews or observations in order to deduce the goodness of their approach through qualitative data. There are some works based on empirical studies aimed at studying the emotional states directly on players, using quantitative data. However, these researches usually consider the data analysis as a classification problem involving, mainly, the game events. Our goal is to understand how the feelings, experienced by the players, can be automatically deducted, and how these emotional states can be used to improve the game quality. In order to pursue this purpose, we have measured the mental states using physiological signals in order to return a set of quantitative values used to identify the players emotions. The most common ways to identify emotions are: to use a discrete set of labels (e.g., joy, anger), or to assess them inside an n-dimensional vector space. Albeit the most natural way to describe the emotions is to represent them through their name, the latter approach provides a quantitative result that can be used to define the new game status. In this thesis, we propose a framework aimed at an automatic assessment, using physiological data, of emotions in a 2-dimensional space, structured by valence and arousal vectors. The former may vary between pleasure and displeasure, while the latter defines the level of physiological activation. As a consequence, we have considered as most effective to infer the players\u2019 mental states, the following physiological data: electrocardiography (ECG), electromyography on 5 facial muscles (Facial EMG), galvanic skins response (GSR), and respiration intensity/rate. We have recorded a video, during a set of game sessions, of the player's face and of her gameplay. To acquire the affective information, we have shown the recorded video and audio to the player, and we have asked to self-assess her/his emotional state over the entire game on the valence and arousal vectors presented above. Starting from this framework, we have conducted two sets of experiments. In the first experiment, our aim was to validate the procedure. We have collected the data of 10 participants while playing at 4 platform games. We have also analyzed the data to identify the emotion pattern of the player during the gaming sessions. The analysis has been conducted in two directions: individual analysis (to find the physiological pattern of an individual player), and collective analysis (to find the generic patterns of the sample population). The goal of the second experiment has been to create a dataset of physiological information of 33 players, and to extend the data analysis and the results provided by the pilot study. We have asked the participants to play at 2 racing games in two different environments: on a standard monitor and using a head mounted display for Virtual Reality. After we have collected the information useful to the dataset creation, we have analyzed the data focusing on individual analysis. In both analyses, the self-assessment and the physiological data have been used in order to infer the emotional state of the players in each moment of the game sessions, and to build a prediction model of players' emotions using Machine Learning techniques. Therefore, the three main contributions of this thesis are: to design a novel framework for study the emotions of video game players, to develop an open-source architecture and a set of software able to acquire the physiological signals and the affective states, to create an affective dataset using racing video games as stimuli, to understand which physiological conditions could be the most relevant in order to determine the players' emotions, and to propose a method for the real-time prediction of a player's mental state during a video game session. The results to suggest that it is possible to design a model that fits with player's characteristics, predicting her emotions. It could be an effective tool available to game designers who can introduce innovative features to their games

    Multi-agent simulations for the evaluation of Looting Systems design in MMOG and MOBA games

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    Massively Multiplayer Online Games (MMOGs) are persistent worlds where a huge number of players interact with each other in order to improve their avatar's characteristics. Multiplayer Online Battle Arenas (MOBAs) - also known as Action Real-Time Strategy (ARTS) games - are video games in which each player controls a single character in one of two competing teams; goal of the game is to destroy the antagonist team. In both genres, players\u2019 characters typically exploit their special abilities, which contribute to the overall strategy of their faction or team. Social interactions among players are at the core of both these game types, and a careful design of the game social architecture is a key factor in determining the success of a specific product. The attention of researchers and practitioners has, till now, focused mainly on several game features, while others have been considered secondary, possibly underestimating their importance in terms of the game overall quality. For instance, in MMOGs, loot items (a type of in-game reward) are not distributed evenly, and the competition for getting the best prize, often, is left in the hands of the players. To handle this issue, players have created resource allocation algorithms called Looting Systems (LS). Generally, the adoption of a specific LS is based on a gentlemen's agreement among the players, and the respect of its outcomes largely depends on mutual trust. Quite recently, ad hoc forms of LS have been introduced also into MOBAs. This topic has received moderate attention by the scientific community, anyway, we sustain that a LS could influence the players' behaviour and, if mismanaged, possibly the survival of the whole community of players in a game. Hence, detecting and tracking the hidden social effects of apparently minor features could become a critical factor in the development of games genres which heavily depend on the quality of social interactions among players. To tackle this issue, we present a simulative study - based on Agent-Based Model (ABM) techniques - of the effects of the adoption of different LSs on heterogeneous player bases. The final goal of our study is to provide several guidelines and hints about the design of LSs to game designers working on MMOs or MOBAs
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