What did I do Wrong in my MOBA Game?: Mining Patterns Discriminating Deviant Behaviours

International audienceThe success of electronic sports (eSports), where professional gamers participate in competitive leagues and tournaments , brings new challenges for the video game industry. Other than fun, games must be difficult and challenging for eSports professionals but still easy and enjoyable for amateurs. In this article, we consider Multi-player Online Battle Arena games (MOBA) and particularly, " Defense of the Ancients 2 " , commonly known simply as DOTA2. In this context, a challenge is to propose data analysis methods and metrics that help players to improve their skills. We design a data mining-based method that discovers strategic patterns from historical behavioral traces: Given a model encoding an expected way of playing (the norm), we are interested in patterns deviating from the norm that may explain a game outcome from which player can learn more efficient ways of playing. The method is formally introduced and shown to be adaptable to different scenarios. Finally, we provide an experimental evaluation over a dataset of 10, 000 behavioral game traces

Relevant Independent Variables on MOBA Video Games to Train Machine Learning Algorithms

Popularity of MultiplayerOnlineBattle Arena (MOBA)video gameshas grown considerably, its popularity as well as the complexity of their playability, have attracted the attention in recent years of researchers from various areas of knowledge and in particular how they have resorted to different machine learning techniques. The papers reviewed mainly look for patterns in multidimensional data sets. Furthermore, these previous researches do not present a way to select the independent variables(predictors)to train the models. For this reason, this paper proposes a listof variables based on the techniques used and the objectives of the research. It allows to provide a set of variables to find patterns applied in MOBA videogames.In order to get the mentioned list,the consulted workswere groupedbythe used machine learning techniques, ranging from rule-based systems to complex neural network architectures. Also, a grouping technique is applied based on the objective of each research proposed

Rational Agent Architecture to Recommend which Item to Buy in MOBA Videogames

Los videojuegos multijugador de arena de batalla en línea (MOBA), es un genero de videojuegos que durante la última década han ganado popularidad en la escena competitiva de los E-Sports. Este incremento en su popularidad y la complejidad propia de los mismos han llamado la atención de investigadores en todas las áreas del conocimiento, incluyendo la Inteligencia Artificial. Dichos investigadores han utilizado una amplia variedad de técnicas de Aprendizaje de Maquina buscando mejorar la experiencia de diversos usuarios -jugadores novatos, jugadores expertos, espectadores, entre otros- a través de modelos de predicción, sistemas de recomendación y, aunque se han utilizado técnicas de optimización; estas últimas han sido las menos utilizadas en los videojuegos tipo MOBA. Por ello, el presente trabajo de investigación propone la arquitectura de un agente racional capaz de recomendar a un jugador que objeto comprar para aumentar sus probabilidades de ganar una partida, utilizando una técnica de optimización para la generación de recomendaciones. En la arquitectura propuesta, el agente percibe su ambiente con la información disponible en el API del videojuego League of Legends -uno de los MOBA mas populares actualmente-. Tal información es interpretada por una Regresión Logística que durante las etapas tempranas del juego demostró tener una precisión alrededor de 0.975. A su vez, la técnica de optimización seleccionada para generar la sugerencia fue GRASP; en promedio cada sugerencia es generada en 0.36 segundos, estas sugerencias durante la experimentación lograron aumentar la probabilidad de ganar una partida en promedio 5.2x.Multiplayer online battle arena (MOBA) video games are a genre of video games that during the last decade have gained popularity in the competitive E-Sports scene. This increase in popularity and MOBA’s complexity have attracted the attention of researchers in all areas of knowledge, including Artificial Intelligence (AI). AI researchers have used a wide variety of Machine Learning techniques seeking to improve the experience of various users - novice players, expert players, spectators, among others - through prediction models, recommendation systems and optimization algorithms. However, optimization algorithms have been the least used in MOBA videogames. For that reason, this research proposes the architecture of a rational agent capable of recommending to a player what item to buy to increase his probabilities of winning a game, using an optimization technique for generating recommendations. In the proposed architecture, the agent perceives his environment with the information available in the API of League of Legends -currently, one of the most popular MOBA videogames -. Such information is interpreted by a Logistic Regression that during the early stages of the game was shown to have an accuracy around 0.975. Additionally, the optimization technique selected to generate the suggestion was GRASP. On average each suggestion is generated in 0.36 seconds. During experimentation, these suggestions increase the probability of winning a game on average 5.2x.Magíster en Inteligencia ArtificialMaestrí

Rational Agent Architecture to Recommend which Item to Buy in MOBA Videogames

Los videojuegos multijugador de arena de batalla en línea (MOBA), es un genero de videojuegos que durante la última década han ganado popularidad en la escena competitiva de los E-Sports. Este incremento en su popularidad y la complejidad propia de los mismos han llamado la atención de investigadores en todas las áreas del conocimiento, incluyendo la Inteligencia Artificial. Dichos investigadores han utilizado una amplia variedad de técnicas de Aprendizaje de Maquina buscando mejorar la experiencia de diversos usuarios -jugadores novatos, jugadores expertos, espectadores, entre otros- a través de modelos de predicción, sistemas de recomendación y, aunque se han utilizado técnicas de optimización; estas últimas han sido las menos utilizadas en los videojuegos tipo MOBA. Por ello, el presente trabajo de investigación propone la arquitectura de un agente racional capaz de recomendar a un jugador que objeto comprar para aumentar sus probabilidades de ganar una partida, utilizando una técnica de optimización para la generación de recomendaciones. En la arquitectura propuesta, el agente percibe su ambiente con la información disponible en el API del videojuego League of Legends -uno de los MOBA mas populares actualmente-. Tal información es interpretada por una Regresión Logística que durante las etapas tempranas del juego demostró tener una precisión alrededor de 0.975. A su vez, la técnica de optimización seleccionada para generar la sugerencia fue GRASP; en promedio cada sugerencia es generada en 0.36 segundos, estas sugerencias durante la experimentación lograron aumentar la probabilidad de ganar una partida en promedio 5.2x.Multiplayer online battle arena (MOBA) video games are a genre of video games that during the last decade have gained popularity in the competitive E-Sports scene. This increase in popularity and MOBA’s complexity have attracted the attention of researchers in all areas of knowledge, including Artificial Intelligence (AI). AI researchers have used a wide variety of Machine Learning techniques seeking to improve the experience of various users - novice players, expert players, spectators, among others - through prediction models, recommendation systems and optimization algorithms. However, optimization algorithms have been the least used in MOBA videogames. For that reason, this research proposes the architecture of a rational agent capable of recommending to a player what item to buy to increase his probabilities of winning a game, using an optimization technique for generating recommendations. In the proposed architecture, the agent perceives his environment with the information available in the API of League of Legends -currently, one of the most popular MOBA videogames -. Such information is interpreted by a Logistic Regression that during the early stages of the game was shown to have an accuracy around 0.975. Additionally, the optimization technique selected to generate the suggestion was GRASP. On average each suggestion is generated in 0.36 seconds. During experimentation, these suggestions increase the probability of winning a game on average 5.2x.Magíster en Inteligencia ArtificialMaestrí

Esports Enthusiasts and Gamers: Motivations, Behaviors, and Attitudes Towards Gambling

This study examined what the driving factors behind why people watch esports and play video games, and their views on casino gambling. This research takes into account several motivational models and theories for video game and media consumption, including the Uses and Gratifications Theory. In addition, motivations and behaviors in regards to gambling were also examined. Although there is plenty of research on gambling motivations, none looks primarily at how esports and video game enthusiasts in specific feel about gambling. In-depth Interviews were conducted on esports and video game enthusiasts to understand what they enjoy about esports and gaming, and what they like and don’t like about casino gaming. Results showed a wide range of motivations behind video game play, but challenge, skill, and socialization were the most common. For gameplay itself, people tended to really enjoy teamwork and collaboration. None of the participants gambled too often, and cited a lack of interactivity and value as primary reasons. One aspect of casino games that many found frustrating, is that their decisions seem to rarely have an impact on the outcome of a game, unlike video games. With video games, nearly each press of the button has a degree of significance. Casinos and casino game manufacturers alike should examine what it is that drives people to play video games and watch esports, and import those qualities into their casino gaming experience

What did I do Wrong in my MOBA Game?: Mining Patterns Discriminating Deviant Behaviours

International audienceThe success of electronic sports (eSports), where professional gamers participate in competitive leagues and tournaments , brings new challenges for the video game industry. Other than fun, games must be difficult and challenging for eSports professionals but still easy and enjoyable for amateurs. In this article, we consider Multi-player Online Battle Arena games (MOBA) and particularly, " Defense of the Ancients 2 " , commonly known simply as DOTA2. In this context, a challenge is to propose data analysis methods and metrics that help players to improve their skills. We design a data mining-based method that discovers strategic patterns from historical behavioral traces: Given a model encoding an expected way of playing (the norm), we are interested in patterns deviating from the norm that may explain a game outcome from which player can learn more efficient ways of playing. The method is formally introduced and shown to be adaptable to different scenarios. Finally, we provide an experimental evaluation over a dataset of 10, 000 behavioral game traces

Unmet goals of tracking: within-track heterogeneity of students' expectations for

Educational systems are often characterized by some form(s) of ability grouping, like tracking. Although substantial variation in the implementation of these practices exists, it is always the aim to improve teaching efficiency by creating homogeneous groups of students in terms of capabilities and performances as well as expected pathways. If students’ expected pathways (university, graduate school, or working) are in line with the goals of tracking, one might presume that these expectations are rather homogeneous within tracks and heterogeneous between tracks. In Flanders (the northern region of Belgium), the educational system consists of four tracks. Many students start out in the most prestigious, academic track. If they fail to gain the necessary credentials, they move to the less esteemed technical and vocational tracks. Therefore, the educational system has been called a 'cascade system'. We presume that this cascade system creates homogeneous expectations in the academic track, though heterogeneous expectations in the technical and vocational tracks. We use data from the International Study of City Youth (ISCY), gathered during the 2013-2014 school year from 2354 pupils of the tenth grade across 30 secondary schools in the city of Ghent, Flanders. Preliminary results suggest that the technical and vocational tracks show more heterogeneity in student’s expectations than the academic track. If tracking does not fulfill the desired goals in some tracks, tracking practices should be questioned as tracking occurs along social and ethnic lines, causing social inequality

Esa 12th Conference: Differences, Inequalities and Sociological Imagination: Abstract Book

Esa 12th Conference: Differences, Inequalities and Sociological Imagination: Abstract Boo