Interpretable Contextual Team-aware Item Recommendation: Application in Multiplayer Online Battle Arena Games

The video game industry has adopted recommendation systems to boost users interest with a focus on game sales. Other exciting applications within video games are those that help the player make decisions that would maximize their playing experience, which is a desirable feature in real-time strategy video games such as Multiplayer Online Battle Arena (MOBA) like as DotA and LoL. Among these tasks, the recommendation of items is challenging, given both the contextual nature of the game and how it exposes the dependence on the formation of each team. Existing works on this topic do not take advantage of all the available contextual match data and dismiss potentially valuable information. To address this problem we develop TTIR, a contextual recommender model derived from the Transformer neural architecture that suggests a set of items to every team member, based on the contexts of teams and roles that describe the match. TTIR outperforms several approaches and provides interpretable recommendations through visualization of attention weights. Our evaluation indicates that both the Transformer architecture and the contextual information are essential to get the best results for this item recommendation task. Furthermore, a preliminary user survey indicates the usefulness of attention weights for explaining recommendations as well as ideas for future work. The code and dataset are available at: https://github.com/ojedaf/IC-TIR-Lol

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

Implementasi Dynamic Difficulty Adjustment Pada Racing Game Menggunakan Metode Behaviour Tree

Video games merupakan hiburan dan tantangan. Tanpa tantangan video games akan mudah diselesaikan dan membosankan. Namun apabila tantangan terlalu sulit dapat membuat pemain frustasi dan menyerah. Hal ini berhubungan dengan flow-state yaitu ketika kemampuan pemain dan tantangan dari game setara. Pada umumnya setiap game menyediakan pengaturan tingkat kesulitan. Tingkat kesulitan yang disediakan biasanya disediakan dalam bentuk pilihan dari tingkat kesulitan mudah (Easy), sedang (Medium), dan sulit (Hard). Sayangnya model pengaturan seperti ini bersifat statis sehingga menimbulkan ketidaksetaraan antara pemain dan tantangan dari game. Untuk menyelesaikan masalah tersebut dynamic difficulty adjustment (DDA) diterapkan dalam penelitian ini. DDA adalah alternatif dari pengatur tingkat kesulitan statis yang harus ditentukan oleh pemain sebelum memulai permainan, dengan adanya DDA pemain tidak perlu repot mengatur tingkat kesulitan sebelum bermain. DDA berfungsi sebagai pengatur tingkat kesulitan yang bekerja secara otomatis berdasarkan kemampuan pemain. Untuk mendukung penerapan DDA, behaviour tree digunakan untuk membantu proses adaptasi AI terhapadap kemampuan pemain. Pengujian dilakukan dengan uji coba balap sebanyak 3 lap dan dicatat nilai jarak tempuh dari setiap checkpoint yang dilewati. Nilai jarak dari masing – masing uji akan dijumlahkan dan dihitung rata-rata selisih jarak. Dari pengujian yang dilakukan didapatkan hasil penerapan behaviour tree dan DDA menghasilkan permaninan yang tidak membosankan dan tidak terlalu sulit untuk pemain. Behaviour tree dan DDA menghasilkan kemampuan AI lawan yang dinamis dan mampu menyesuaikan kemampuan dari pengguna

IMPLEMENTATION OF A PRE-ASSESSMENT MODULE TO IMPROVE THE INITIAL PLAYER EXPERIENCE USING PREVIOUS GAMING INFORMATION

The gaming industry has become one of the largest and most profitable industries today. According to market research, the industry revenues will pass $200 Billion and are expected to reach another$20 Billion in 2024. With the industry growing rapidly, players have become more demanding, expecting better content and quality. This means that game studios need new and innovative ways to make their games more enjoyable. One technique used to improve the player experience is DDA (Dynamic Difficulty Adjustment). It leverages the current player state to perform different adjustments during the game to tune the difficulty delivered to the player to be more in line with their expectations and capabilities. In this thesis, we will explore and test the ability to obtain the difficulty level in which a player should be placed initially, by using previous gaming information from platforms like Steam, combined with different machine learning (ML) algorithms and data analyses., In doing so, we can create a pre-assessment of the player as a way of improving DDA’s initial state and the overall gaming experience of players

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

Exploring player experience and social networks in MOBA Games: The case of League of Legends

A pesar de la popularidad de los juegos de arena de combate multijugador en línea (MOBA en inglés) como League of Legends (LoL), tanto la experiencia de jugador (PE) que proporciona este género relativamente reciente como las redes sociales que se generan a su alrededor siguen, en gran medida, inexplorados. Con el incremento del tiempo que los jugadores dedican a este tipo de juegos competitivos en línea, los impactos positivos y negativos de hacerlo cobran relevancia; es, por lo tanto, importante entender cómo se estructura dicha experiencia para abordar de forma sistemática los mecanismos que desencadenan respuestas de los jugadores. El presente trabajo empieza obteniendo y caracterizando una muestra de jugadores de League of Legends y sigue con el uso de las variables resultantes y de la estructura de las relaciones sociales como entradas para explorar su relación con la experiencia de los jugadores. Al fin y al cabo, la PE es básica para involucrar al jugador y, por lo tanto, es clave para el éxito de cualquier juego digital. Los resultados muestran, entre otros, cómo los jugadores de League of Legends perciben el juego como “justo” para su nivel de competencia en cualquier rango, mientras que su afinidad respecto a los compañeros se ve afectada por la estructura social. La empatía y los sentimientos negativos, no obstante, no parecen verse afectados por la composición del equipo. Entender la experiencia del jugador en League of Legends puede no tan sólo ser útil para mejorar el propio LoL o los juegos de tipo MOBA, sino también para desarrollar juegos más inmersivos a la vez que se mejora su calidad. A medida que los juegos competitivos online se convierten rápidamente en una de las mayores actividades colectivas humanas a nivel global, la investigación sobre la experiencia del jugador adquiere también una importancia crucial

Adaptive Game Input Using Knowledge of Player Capability: Designing for Individuals with Different Abilities

The application of video games has been shown to be valuable in medical interventions, such as the use of Active Video Games in physical therapy. Because patients requiring physical therapy present with both highly variable physical capabilities and unique therapeutic goals, developers of rehabilitation intervention games face the challenge of creating flexible games that they can individualize to each player’s particular needs. This thesis proposes an approach to this problem by identifying and addressing two issues concerning therapy AVG game design. First, regarding the difficulties of individualizing software, a particular complication in the development of AVGs for therapy is the increased complexity of writing input routines based on human body motion, which provides a much larger and more complex domain than traditional, discrete-input game controllers. Second, the primary difficulty in individualizing a therapy game experience to an individual player is that developers must program software with static routines that cannot be modified once compiled and released. Overcoming this aspect of software development is a prime concern that adaptive games research aims to address. The System for Unified Kinematic Input (SUKI) is a software library that addresses both of these concerns. SUKI enables games to adapt to players’ specific therapeutic goals by mapping arbitrary human body movement input to game mechanics at runtime, allowing user-defined body motions to drive gameplay without requiring any change to the software. Additionally, the SUKI library implements a dynamic profile system that alters the game’s configuration based on known physical capabilities of the player and updates this profile based on the player’s demonstrated ability during play. Within the context of the study of adaptive games, the following research presents the details of this approach and demonstrates the versatility and extensibility that it can provide in adapting AVG games to meet individual player needs.https://doi.org/10.17918/D8R94VM.S., Digital Media -- Drexel University, 201

Adaptive Technologies in Digital Games: The Influence of Perception of Adaptivity on Immersion

Digital games with adaptive technologies offer more tailored experiences to their players, as gameplay is based on the players' performances and behaviours in the game. This could potentially lead to better gaming experiences. Though it is also possible that just the mere expectation of clever AI could affect players' first impressions and subsequently their perceived experiences. At the present moment, there is little empirical evidence supporting this claim. This research aims to gather empirical evidence to test the hypothesis that players' expectations of an adaptive digital game have an effect on their immersion. For this, three studies were conducted. First, preferences were explored as a form of expectations that could influence immersion. The results show no effect of preferences with regards to the visual perspective on immersion. A more controlled manipulation in the form of game descriptions was then used in the subsequent experiments. Participants played a game without adaptive features while being told that the game was adapting to their performance. As a result, players who believed that the game had adaptive AI experienced higher levels of immersion than the players who were not aware of it. Similarly, when playing the game twice people felt more immersed in the session that was supposedly adapting to their behaviour, in spite of experiencing the same gameplay as in the other session. This effect was then explored in more detail in games with adaptive features. For this, two games were developed to adapt in two distinct ways to players' performance in the game. Immersion was affected differently depending on the precision of information about these adaptive features. More detailed information prompts players to change their tactics to incorporate the adaptation into their play and experience the benefits of this feature. Merely being aware of the adaptation leads to more immersion, regardless of its presence in the game. Similarly, the presence of an adaptive feature in the game leads to heightened sense of immersion, which is enhanced by the precision of information players receive about it. Evidence also suggests that this effect is durable. Overall, this research provides empirical evidence to support the hypothesis that players' expectations of adaptive features in single-player games have a positive effect on immersion. This is a valuable contribution to the theoretical understanding of immersion, while it also provides some insights into the potential precautions that should be considered when conducting experiments into player experience in the lab and `in the wild', both in academic studies and during player testing sessions run by game developers