Data extraction methodology to improve the gameplay experience in video games and to analyse the user's profile behaviour and its evolution

Los videojuegos son la principal fuente de entretenimiento en nuestros días y la que más beneficios genera en la industria de los contenidos audiovisuales. Para las compañías que desarrollan videojuegos es muy importante saber el perfil de los usuarios que adquieren sus videojuegos para, posteriormente, desarrollar contenido específico para dichos usuarios. Los usuarios, por otra parte, exigen un mayor realismo en la inteligencia artificial de los enemigos y un mayor nivel de dificultad, todo ello unido a una elevada capacidad de personalización del modo de juego. En el ámbito de la inteligencia artificial y de la personalización existen videojuegos con mecánicas dinámicas que hacen que cada partida tenga una experiencia única. En este trabajo se pretende, mediante el diseño y programación de un videojuego, abordar dichos problemas para conseguir una metodología que sirva de ayuda en este campo. Para ello, se recopilarán estadísticas de uso del videojuego que serán analizadas para determinar las mejoras realizables dentro del propio videojuego. Con estas estadísticas, se realizar a un análisis de los perfiles de los usuarios presentes en el videojuego con el objetivo de saber los distintos tipos de usuarios que hay en función de su nivel de habilidad y en función de su estilo de juego. Todo esto se realizar a con el objetivo de proporcionar una experiencia más gratificante de cara al usuario. De esta forma, se podrán crear mecánicas dinámicas de juego en función de las acciones que hayan realizado cada uno de los usuarios. Finalmente, este trabajo aprovecha esta información para aportar posibles soluciones para mejorar la jugabilidad del propio videojuego y para clasificar a los usuarios en función de la evolución de su perfil utilizando los resultados extraídos del análisis realizado. Para realizar el análisis propuesto se han empleado técnicas de Data Mining no supervisado y series temporales.Video games are the main source of entertainment these days and the most pro table industry that generates audiovisual contents. On the one hand, video game companies consider important to understand their users pro le in order to develop especi c content for them. On the other hand, current users require some arti cial intelligence improvements and gameplay customization to enrich the game experience. In the eld of arti cial intelligence and gameplay customization, there are several video games with dynamic gameplay mechanics that make each game looks like a new and unique experience. The goal of this work, through the design and programming of a video game, is to get a methodology that helps in this eld. To do this, usage video game statistics are collected to be analyzed in order to determine the achievable improvements within the video game itself. With these statistics, an analysis of the users pro les present in the video game is performed in order to know the di erent types of users depending on their skill level and their play style. All this is done with the aim of providing a more rewarding experience for the user. Thus, it can be created dynamic gameplay mechanics based on the actions of each user. Finally, this work uses this information to provide possible solutions to improve the gameplay of the video game and to classify the users according to their pro le evolution by using the results extracted from the previous analysis. To perform the proposed analysis, unsupervised data mining and time-series techniques have been employed

GECKA3D: A 3D Game Engine for Commonsense Knowledge Acquisition

Commonsense knowledge representation and reasoning is key for tasks such as artificial intelligence and natural language understanding. Since commonsense consists of information that humans take for granted, gathering it is an extremely difficult task. In this paper, we introduce a novel 3D game engine for commonsense knowledge acquisition (GECKA3D) which aims to collect commonsense from game designers through the development of serious games. GECKA3D integrates the potential of serious games and games with a purpose. This provides a platform for the acquisition of re-usable and multi-purpose knowledge, and also enables the development of games that can provide entertainment value and teach players something meaningful about the actual world they live in

Affective games:a multimodal classification system

Affective gaming is a relatively new field of research that exploits human emotions to influence gameplay for an enhanced player experience. Changes in player’s psychology reflect on their behaviour and physiology, hence recognition of such variation is a core element in affective games. Complementary sources of affect offer more reliable recognition, especially in contexts where one modality is partial or unavailable. As a multimodal recognition system, affect-aware games are subject to the practical difficulties met by traditional trained classifiers. In addition, inherited game-related challenges in terms of data collection and performance arise while attempting to sustain an acceptable level of immersion. Most existing scenarios employ sensors that offer limited freedom of movement resulting in less realistic experiences. Recent advances now offer technology that allows players to communicate more freely and naturally with the game, and furthermore, control it without the use of input devices. However, the affective game industry is still in its infancy and definitely needs to catch up with the current life-like level of adaptation provided by graphics and animation

Towards Structured Analysis of Broadcast Badminton Videos

Sports video data is recorded for nearly every major tournament but remains archived and inaccessible to large scale data mining and analytics. It can only be viewed sequentially or manually tagged with higher-level labels which is time consuming and prone to errors. In this work, we propose an end-to-end framework for automatic attributes tagging and analysis of sport videos. We use commonly available broadcast videos of matches and, unlike previous approaches, does not rely on special camera setups or additional sensors. Our focus is on Badminton as the sport of interest. We propose a method to analyze a large corpus of badminton broadcast videos by segmenting the points played, tracking and recognizing the players in each point and annotating their respective badminton strokes. We evaluate the performance on 10 Olympic matches with 20 players and achieved 95.44% point segmentation accuracy, 97.38% player detection score ([email protected]), 97.98% player identification accuracy, and stroke segmentation edit scores of 80.48%. We further show that the automatically annotated videos alone could enable the gameplay analysis and inference by computing understandable metrics such as player's reaction time, speed, and footwork around the court, etc.Comment: 9 page

Methodologies for evaluating the playability of mobile games:systematic literature review

Tiivistelmä. The gaming industry has been growing rapidly during the past years due to the interest of the new generations in mobile gaming. To deliver a great experience for the gamers, it is required for the gaming companies to produce games that are challenging but at the same time easy to play. To achieve this, it is required to understand the factors that affect the gaming experience. Playability is a term that is used to understand the usability of a game and its experience. The purpose of this thesis was to understand what is known related to the playability of mobile games and to identify the methodologies that are used by the community to evaluate this phenomenon. To find the answers to these questions, it was performed a systematic literature review (SLR) using the databases Scopus, IEEE Xplore, and Web of Science. After conducting the SLR, 1,390 studies related to the playability of mobile games were found from which 27 were identified as primary studies of this research. From the data collected from the primary studies, there were identified 12 different methodologies that are used for evaluating the playability of mobile games. The methodologies that are most suitable to assess the playability of mobile games are heuristic evaluation and playtesting. Other methodologies can be used for evaluating the playability of mobile games, but they must include a set of heuristics that allows evaluating the playability. The limitations of the research were mentioned, and it was proposed topics for future research of this field. The contribution of this thesis is the summarizing of the current methodologies that are used to understand and evaluate the playability of mobile games. The results of this thesis are valuable for game developers, game designers, and game usability practitioners

Making CNNs for Video Parsing Accessible

The ability to extract sequences of game events for high-resolution e-sport games has traditionally required access to the game's engine. This serves as a barrier to groups who don't possess this access. It is possible to apply deep learning to derive these logs from gameplay video, but it requires computational power that serves as an additional barrier. These groups would benefit from access to these logs, such as small e-sport tournament organizers who could better visualize gameplay to inform both audience and commentators. In this paper we present a combined solution to reduce the required computational resources and time to apply a convolutional neural network (CNN) to extract events from e-sport gameplay videos. This solution consists of techniques to train a CNN faster and methods to execute predictions more quickly. This expands the types of machines capable of training and running these models, which in turn extends access to extracting game logs with this approach. We evaluate the approaches in the domain of DOTA2, one of the most popular e-sports. Our results demonstrate our approach outperforms standard backpropagation baselines.Comment: 11 pages, 6 figures, Foundations of Digital Games 201