163 research outputs found
BPCoach: Exploring Hero Drafting in Professional MOBA Tournaments via Visual Analytics
Hero drafting for multiplayer online arena (MOBA) games is crucial because
drafting directly affects the outcome of a match. Both sides take turns to
"ban"/"pick" a hero from a roster of approximately 100 heroes to assemble their
drafting. In professional tournaments, the process becomes more complex as
teams are not allowed to pick heroes used in the previous rounds with the
"best-of-N" rule. Additionally, human factors including the team's familiarity
with drafting and play styles are overlooked by previous studies. Meanwhile,
the huge impact of patch iteration on drafting strengths in the professional
tournament is of concern. To this end, we propose a visual analytics system,
BPCoach, to facilitate hero drafting planning by comparing various drafting
through recommendations and predictions and distilling relevant human and
in-game factors. Two case studies, expert feedback, and a user study suggest
that BPCoach helps determine hero drafting in a rounded and efficient manner.Comment: Accepted by The 2024 ACM SIGCHI Conference on Computer-Supported
Cooperative Work & Social Computing (CSCW) (Proc. CSCW 2024
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
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
Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error
Methods for dynamic difficulty adjustment allow games to be tailored to
particular players to maximize their engagement. However, current methods often
only modify a limited set of game features such as the difficulty of the
opponents, or the availability of resources. Other approaches, such as
experience-driven Procedural Content Generation (PCG), can generate complete
levels with desired properties such as levels that are neither too hard nor too
easy, but require many iterations. This paper presents a method that can
generate and search for complete levels with a specific target difficulty in
only a few trials. This advance is enabled by through an Intelligent
Trial-and-Error algorithm, originally developed to allow robots to adapt
quickly. Our algorithm first creates a large variety of different levels that
vary across predefined dimensions such as leniency or map coverage. The
performance of an AI playing agent on these maps gives a proxy for how
difficult the level would be for another AI agent (e.g. one that employs Monte
Carlo Tree Search instead of Greedy Tree Search); using this information, a
Bayesian Optimization procedure is deployed, updating the difficulty of the
prior map to reflect the ability of the agent. The approach can reliably find
levels with a specific target difficulty for a variety of planning agents in
only a few trials, while maintaining an understanding of their skill landscape.Comment: To be presented in the Conference on Games 202
- …