An analysis of winning streak's effects in language course of “Duolingo” *

This paper explores the effects of the winning streak on users' motivation and engagement in Duolingo's language course. The winning streak has been used in sport and video games to describe a consecutive number of successful actions and increase players' attention to complete their goal. Similarly, in gamified education system, the winning streak is employed as a game element to boost up motivation of learners. By applying game refinement theory as an assessment method, enjoyment of two user groups in Duolingo is measured to compare. The results indicate that the winning streak can boost up learners' motivation and attention to complete their goals. It also expressed that the winning streak is more significant for advanced learners who are in the high level of milestone than those who are in the low level of milestone

Demographics and consumption analysis of virtual products in the videogame industry: the Dota 2 case study

Le développement de l’industrie du jeu vidéo s’est énormément développé ces dernières années. Dans le cas des MOBA, Dota 2 est l’un des jeux vidéo les plus attractifs parmi les joueurs du monde entier. Ce jeu vidéo a été traduit en 26 langues et sa monétisation est donnée par les achats supplémentaires des joueurs, puisque le téléchargement et la participation du jeu sont gratuits. Son événement principal, le Dota 2 International, est le tournoi d’e-sport avec la plus forte participation et des prix pouvant aller jusqu’à 34 millions de dollars (USD), qui sont largement financés par les Battle pass achetés par les joueurs. Ces caractéristiques nous ont motivés à générer une étude de cas, qui cherche à analyser la structure et le fonctionnement de DOTA 2, le profil de ses joueurs et leur investissement en temps et en argent dans le jeu.The development of the video game industry has grown enormously in recent years. In the case of MOBAs1, Dota 2 is one of the most attractive video games among players throughout the world. This video game has been translated into 26 languages and its monetization hinges upon additional purchases of the players, since the game itself is free. Its flagship event, the Dota 2 International, is the e-sports tournament with the highest participation, viewership, and prizes that can go up to 34 million dollars (USD), which is mostly financed by the battle passes purchased by the players. These characteristics have motivated us to study this case by analyzing the structure and operation of DOTA 2, the profile of its players, and their investments in time and money within the game

Computational Theory of Mind for Human-Agent Coordination

In everyday life, people often depend on their theory of mind, i.e., their ability to reason about unobservable mental content of others to understand, explain, and predict their behaviour. Many agent-based models have been designed to develop computational theory of mind and analyze its effectiveness in various tasks and settings. However, most existing models are not generic (e.g., only applied in a given setting), not feasible (e.g., require too much information to be processed), or not human-inspired (e.g., do not capture the behavioral heuristics of humans). This hinders their applicability in many settings. Accordingly, we propose a new computational theory of mind, which captures the human decision heuristics of reasoning by abstracting individual beliefs about others. We specifically study computational affinity and show how it can be used in tandem with theory of mind reasoning when designing agent models for human-agent negotiation. We perform two-agent simulations to analyze the role of affinity in getting to agreements when there is a bound on the time to be spent for negotiating. Our results suggest that modeling affinity can ease the negotiation process by decreasing the number of rounds needed for an agreement as well as yield a higher benefit for agents with theory of mind reasoning.</p

Many-agent Reinforcement Learning

Multi-agent reinforcement learning (RL) solves the problem of how each agent should behave optimally in a stochastic environment in which multiple agents are learning simultaneously. It is an interdisciplinary domain with a long history that lies in the joint area of psychology, control theory, game theory, reinforcement learning, and deep learning. Following the remarkable success of the AlphaGO series in single-agent RL, 2019 was a booming year that witnessed significant advances in multi-agent RL techniques; impressive breakthroughs have been made on developing AIs that outperform humans on many challenging tasks, especially multi-player video games. Nonetheless, one of the key challenges of multi-agent RL techniques is the scalability; it is still non-trivial to design efficient learning algorithms that can solve tasks including far more than two agents ($N \gg 2$), which I name by \emph{many-agent reinforcement learning} (MARL\footnote{I use the world of ``MARL" to denote multi-agent reinforcement learning with a particular focus on the cases of many agents; otherwise, it is denoted as ``Multi-Agent RL" by default.}) problems. In this thesis, I contribute to tackling MARL problems from four aspects. Firstly, I offer a self-contained overview of multi-agent RL techniques from a game-theoretical perspective. This overview fills the research gap that most of the existing work either fails to cover the recent advances since 2010 or does not pay adequate attention to game theory, which I believe is the cornerstone to solving many-agent learning problems. Secondly, I develop a tractable policy evaluation algorithm -- $\alpha^\alpha$-Rank -- in many-agent systems. The critical advantage of $\alpha^\alpha$-Rank is that it can compute the solution concept of $\alpha$-Rank tractably in multi-player general-sum games with no need to store the entire pay-off matrix. This is in contrast to classic solution concepts such as Nash equilibrium which is known to be $PPAD$-hard in even two-player cases. $\alpha^\alpha$-Rank allows us, for the first time, to practically conduct large-scale multi-agent evaluations. Thirdly, I introduce a scalable policy learning algorithm -- mean-field MARL -- in many-agent systems. The mean-field MARL method takes advantage of the mean-field approximation from physics, and it is the first provably convergent algorithm that tries to break the curse of dimensionality for MARL tasks. With the proposed algorithm, I report the first result of solving the Ising model and multi-agent battle games through a MARL approach. Fourthly, I investigate the many-agent learning problem in open-ended meta-games (i.e., the game of a game in the policy space). Specifically, I focus on modelling the behavioural diversity in meta-games, and developing algorithms that guarantee to enlarge diversity during training. The proposed metric based on determinantal point processes serves as the first mathematically rigorous definition for diversity. Importantly, the diversity-aware learning algorithms beat the existing state-of-the-art game solvers in terms of exploitability by a large margin. On top of the algorithmic developments, I also contribute two real-world applications of MARL techniques. Specifically, I demonstrate the great potential of applying MARL to study the emergent population dynamics in nature, and model diverse and realistic interactions in autonomous driving. Both applications embody the prospect that MARL techniques could achieve huge impacts in the real physical world, outside of purely video games

Organizational Learning in the Rise of Machine Learning

Organizational learning (OL) is associated with experience and knowledge in an organization. Information Technology (IT) enables the creation, dissemination, and use of knowledge, and as such, plays an important role in an organization’s learning process. This role has inspired a large body of literature studying the link between OL and IT and the relation between IT and knowledge exploration and exploitation. The recent rise of Machine Learning (ML) with its Deep Learning (DL) capabilities has nevertheless brought about new ways of creating, retaining, and transferring knowledge. I argue that the learning occurring within the machine plays a role in the learning occurring within the organization, calling for revisiting OL in light of this disruptive IT. In this paper, I focus on three different ways in which the machine achieves its learning, namely supervised, unsupervised, and reinforcement learning, and advance propositions on how each impacts OL differently

Competitive Gaming: Design and Community Building

This project attempts to characterize the qualities that make a competitive game successful. By researching the relevant literature and conducting original interviews, we identified three important development principles: 1. Competitive games should be designed for casual play and balanced for competitive play. 2. Understanding who your players are and what they want can greatly assist in the design of competitive games. When a player gets to use a strategy they enjoy at a high level of play, the game becomes more enjoyable. 3. A competitive game is defined by its community. Communities are also vital to the evolution of their playersÂ’ skills. Without a supportive community, players in that community will have a harder time being successful

Professional gaming and work: Challenges, trajectories, and labour market impacts amongst professional gamers

Over the last decade the popularity of video games has risen tremendously. A new industry around professional gaming has emerged alongside this growth in the popularity of video games. In professional gaming, individuals play video games competitively while their matches and games are streamed online to a global audience. As a result of the growth in the sector, compensation for some individuals has reached well into six and seven figures. Knowledge of these salaries has resulted in an influx of individuals interested in working in professional gaming. This study investigates not only those individuals who play video games professionally, but also those who work in the periphery and infrastructure of the gaming industry. This dissertation critically investigates work in the professional gaming industry drawing on qualitative interview data. Two primary questions are asked: What is the nature of work in professional gaming? What are the experiences of individuals who pursue careers and work in this industry? This exploratory research utilizes thirty-four semi-structured interviews with individuals involved in the professional gaming industry. The participants describe a range of challenges, difficulties, and issues they experience both transitioning into and working in this industry. The results suggest that work in professional gaming is rife with exploitation, precarity, and non-standard work arrangements. Pursuing a career in this industry is difficult, and participants often lack social support during their transition from playing video games as leisure to being employed in professional gaming. This lack of social support is not determinative and the participants often accessed their social capital in other ways in order to succeed. Financial difficulties, geographic challenges, and issues with discrimination and sexism were faced by the participants working in this industry. The present study provides important recommendations for future research. Overall, the study sheds new light on the nature of work in this industry – work that many dismiss as simply leisure – revealing tensions, contradictions, and inequalities within it

Forecasting of Economic Value Added in Entertainment Industry

This thesis is to evaluate the past performance and predict the future financial performance on the basis of real data of the entertainment company, Ubisoft Entertainment SA. We describes financial analysis and performance measures, which includes the characteristics of the economic value added. The prediction of economic value added is based on the prediction of financial plan by using Monte Carlo simulation in Excel.Finally, we make a conclusion on the financial situation of the company in the past ten years, evaluate the feasibility of Monte Carlo simulation and the investment feasibility of the company.This thesis is to evaluate the past performance and predict the future financial performance on the basis of real data of the entertainment company, Ubisoft Entertainment SA. We describes financial analysis and performance measures, which includes the characteristics of the economic value added. The prediction of economic value added is based on the prediction of financial plan by using Monte Carlo simulation in Excel.Finally, we make a conclusion on the financial situation of the company in the past ten years, evaluate the feasibility of Monte Carlo simulation and the investment feasibility of the company.154 - Katedra financívelmi dobř