Dynamic Difficulty Adaptation for Heterogeneously Skilled Player Groups in Multiplayer Collaborative Games

This work focuses on the combination of two key concepts: Dynamic Difficulty Adjustment/Adaptation (video games adapting their difficulty according to the in-game performance of players, making themselves easier if the player performs poorly or more difficult if the player performs well) and Collaborative Multiplayer Games (video games where two or more human players work together to achieve a common goal). It considers and analyzes the challenges, potential and possibilities of Dynamic Difficulty Adaptation in Collaborative Multiplayer Games, which has to date been quite unexplored. In particular, it addresses the heterogeneously skilled player groups challenge: players with different skill levels play together in a video game, but how should the game adapt its difficulty if one player performs well when another performs badly? We use previous research on Dynamic Difficulty Adaption in single player games to mold, define and classify general approaches to Dynamic Difficulty Adaptation in collaborative games. We then focus on a subgroup of collaborative games - distinct-role collaborative video games, where we believe there is a viable way to address the heterogeneously skilled player groups challenge. To test our general approach to Dynamic Difficulty Adaptation in collaborative games we present a game that has been exclusively developed for this purpose: Co-op Craft. It is a collaborative game that uses the StarCraft II™ engine and includes three Dynamic Difficulty Adaptation Algorithms. We analyze, justify and classify these algorithms and we also outline other valid alternatives. We had players from different gaming backgrounds test this game both with Dynamic Difficulty Adaptation active and with Dynamic Difficulty Adaptation disabled. We analyzed their performance in both cases and asked their opinion on the matter based on their experience. From the numerical data representing their performance and their impressions we then extract conclusions about our approach and also the potential of including Dynamic Difficulty Adaptation in popular collaborative games of today

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

Component-action deep Q-learning for real-time strategy game AI

Real-time Strategy (RTS) games provide a challenging environment for AI research, due to their large state and action spaces, hidden information, and real-time gameplay. The RTS game StarCraft II has become a new test-bed for deep reinforcement learning (RL) systems using the StarCraft II Learning Environment (SC2LE). Recently the full game of StarCraft II has been approached with a complex multi-agent RL system only possible with extremely large financial investments. In this thesis we will describe existing work in RTS AI and motivate our work adapting the deep Q-learning (DQN) RL algorithm to accommodate the multi-dimensional action-space of the SC2LE. We then present the results of our experiments using custom combat scenarios. First, we compare methods for calculating DQN training loss with action components. Second, we show that policies trained with component-action DQN for five hours perform comparably to scripted policies in smaller scenarios and outperform them in larger scenarios. Third, we explore several ways to transfer policies between scenarios, and show that it is a viable method to reduce training time. We show that policies trained on scenarios with fewer units can be applied to larger scenarios and to scenarios with different unit types with only a small loss in performance

“Niin mut se yrittää kikkailla tiätsä ittesä low hoo pee” : English influence on Finnish-Matrix Computer-Mediated Discourse During a Multi-Player Computer Game

Tutkielma käsittelee suomen ja englannin välistä kielikontaktia nuorten aikuisten keskustelussa monen pelaajan videopeliä pelattaessa. Aineistona käytetään neljän Dota 2 -videopelin ottelun aikana käytyjä keskusteluja, jotka on taltioitu ja translitteroitu. Lisäksi tutkimukseen osallistujia haastateltiin tutkimuksen aikana. Ääniteaineisto jakautuu kahteen tyyppiin: kaksi äänitteistä on pelimuodosta, jossa peli tallentaa sijoitustietoja ja jossa sijoituksissa liian kaukana toisistaan olevat pelaajat eivät voi pelata samassa joukkueessa, kaksi muuta taas pelimuodosta jossa pelaajat voivat vapaasti pelata kenen tahansa kanssa. Tutkimus eroaa lähestymistavaltaan aikaisemmista pelitutkimuksesta, jotka ovat pääasiassa tutkineet erilaista videoaineistoa mm. keskusteluanalyysin ja multimodaalisuuden keinoin. Teoreettinen lähestymistapa perustuu etnografiaan ja autoetnografiaan, jotka korostavat osallistujien kokemusta ja tutkijan osallisuutta tutkimustilanteeseen ja -kohteeseen. Etnografinen lähestymistapa helpotti myös ääniteaineiston tutkimusta ilman peliä tai pelitilannetta kuvaavan video tarjoamaa kontekstia – kontekstia ja paikallisia merkityksiä selvitettiin tarvittaessa osallistujien haastatteluilla. Etnografisen metodologian ansiosta tutkimuksessa saatettiin lisäksi hyödyntää tutkijan olemassaolevaa tietämystä videopelitilanteista ja Dota 2:sta. Suuri osa kielikontakteista aineistossani koostuu koodinvaihdosta. Tutkimuksessa koodinvaihdon määritelmänä käytetty Myers-Scottonin matriisikielikehysmalli (engl. Matrix Language Frame model) osoittautui selitysvoimaisemmaksi kuin aikaisemmissa pelitutkimuksissa käytetyt (esim. Auerin ja Poplackin) mallit. Tutkimus osoittaa, että suomen ja englannin lisäksi kielikontaktia esiintyy myös esim. suomen ja saksan sekä venäjän välillä. Vieraskielisten sanojen käytön lisäksi aineistossa esiintyi niin suomenkielisten kuin vieraskielistenkin sanojen muuntelua ryhmän sisäiseksi erikoissanastoksi sekä Dota 2:n kansainvälisessä pelaajakunnassa vakiintunutta muunneltua erikoissanastoa. Aineistossa esiintyvät keskustelutilanteet ovat varsin samankaltaisia kuin aiemmissa työetnografisissa tutkimuksissa lennonjohdon ja lentokapteenien toiminnasta. Etenkin erikoissanaston jakauma osoittautui hyvin samankaltaiseksi kuin reittilennon laskeutumista edeltävässä tilanteessa