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

From Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI

This paper reviews the field of Game AI, which not only deals with creating agents that can play a certain game, but also with areas as diverse as creating game content automatically, game analytics, or player modelling. While Game AI was for a long time not very well recognized by the larger scientific community, it has established itself as a research area for developing and testing the most advanced forms of AI algorithms and articles covering advances in mastering video games such as StarCraft 2 and Quake III appear in the most prestigious journals. Because of the growth of the field, a single review cannot cover it completely. Therefore, we put a focus on important recent developments, including that advances in Game AI are starting to be extended to areas outside of games, such as robotics or the synthesis of chemicals. In this article, we review the algorithms and methods that have paved the way for these breakthroughs, report on the other important areas of Game AI research, and also point out exciting directions for the future of Game AI