Syftet med föreliggande studie är att undersöka Twitch, en plattform för live-strömmat dataspelande, i förhållande till dess aktörers interaktioner. För att avgränsa studien har ett av de hundratals spel som strömmas på Twitch valts ut; Starcraft II. Genom att behandla Starcraft II & Twitch som ett spänningsfält där polariteter, kapital och dess aktörer strukturerar fältet, utformas en teoretisk modell utifrån Bourdieus fältteori (1984; 1986; 1990) och Goffmans (1959) teorier för mikrosocialt handlande. Det framkommer att fältet går att analysera som ett kulturellt sådant, där professionella gamers och personligheter samt cyniska och oförställda framföranden står i motsatsförhållande. Pro-gamers är fältets rena kulturella kapital medan personligheter korrumperar sitt kapital med socialt och ekonomiskt kapital för att bli erkända på fältet. Cyniska framföranden tar avstånd från ’rollen’ som strömmare medan oförställda framföranden omfamnar den. Fältets viktiga interaktionspunkter och markörer mellan strömmare och tittare är: teammaskopier, moderatorstatus, spelmässigt anknutna symboler för skicklighet, spelregion, emoticons, memes, exkludering och inkludering, Tittarna investerar kapital (tidsmässigt, ekonomiskt, socialt och kulturellt) för att bli del av de olika gemenskaperna som existerar dels på Twitch, men också överlappar på plattformar relaterade till Starcraft II. Sammanfattningsvis struktureras fältet utefter dels spelmässig skicklighet och kunnighet och dels distansen till rollen som strömmare.

Intelligent robots are rational agents. The rationality of robots working cooperatively is significantly different from robots working independently. Cooperation between intelligent robots requires the high level of reasoning and complex interactions for successful operations. The required reasoning process includes knowledge representation and sharing as well as the ability to understand the context of a situation. The reasoning process heavily influences on the planning of deciding what actions need to be taken. Goal deliberation and planning is the process that deals with those requirements. This dissertation investigates the problem of goal deliberation and planning to enable such cooperation between goal-oriented intelligent robots, working as a team. The dissertation then proposes a multi-robot system model that embraces results of the investigation. The proposed model is realized on the top of the platform ‘robot operating system’ (ROS). The implemented system, named ‘goal-oriented multi agent systems’ (GOMAS), is demonstrated with the computer game, StarCraft II. Units in StarCraft II are individually controlled by the GOMAS robots and work cooperatively to attain a set of goals given from operators. The demonstration with the three different scenarios validates that the GOMAS system successfully and efficiently deliberates and plans the given goals.

Clausewitz and Jomini are two big figures in the military science community. Their principles of the concentration of force are still prevalent today in the further development of principles. With this in mind there have been some studies where computer games have been used to improve military training. The study aims to study how the principles of war can lead to victory in the real-time strategy game Starcraft II. The purpose of this study to contribute to the body of scientific knowledge with using computer games to increase the understanding of the principles of war. The method used in this study is a quantitative content analysis to gather data for analysis in the SPSS-program. The results of the study were that all the use of principles that were chosen, with the exception of surprise, correlated with victory. It also showed that spatial ability leads to higher win probability, this find lowers the reliability of the study since it cannot prove to what extent this leads to victory. However, this result is not applicable in the physical world, it can only contribute to a theoretical understanding of the principles of war.

The research problem of this article is the Game AI agent of StarCraft II based on Deep Reinforcement Learning (DRL). StarCraft II is viewed as the most challenging Real-time Strategy (RTS) game for now, and it is also the most popular game where researchers are developing and improving AI agents. Building AI agents of StarCraft II can help researchers on machine learning figure out the weakness of DRL and improve this series of algorithms. In 2018, DeepMind and Blizzard developed the StarCraft II Learning Environment (PySC2) to enable researchers to promote the development of AI agents. DeepMind started to develop a new project called AlphaStar after AlphaGo based on DRL, while several laboratories also published articles about the AI agents of StarCraft II. Most of them are researching on the AI agents of Terran and Zerg, which are two of three races in StarCraft II. AI agents show high-level performance compared with most StarCraft II players. However, the performance is far from defeating E-sport players because Game AI for StarCraft II has large observation space and large action space. However, there is no publication on Protoss, which is the remaining and most complicated race to deal with (larger action space, larger observation space) for AI agents due to its characteristics. Thus, in this paper, the research question is whether the AI agent of Protoss, which is developed by the model based on DRL, for a full-length game on a particular map can defeat the high-level built-in cheating AI. The population of this research design is the StarCraft II AI agents that researchers built based on their DRL models, while the sample is the Protoss AI agent in this paper. The raw data is from the game matches between the Protoss AI agent and built-in AI agents. PySC2 can capture features and numerical variables in each match to obtain the training data. The expected outcome is the model based on DRL, which can train a Protoss AI agent to defeat high-level game AI agents with the win rate. The model includes the action space of Protoss, the observation space and the realization of DRL algorithms. Meanwhile, the model is built on PySC2 v2.0, which provides additional action functions. Due to the complexity and the unique characteristics of Protoss in StarCraft II, the model cannot be applied to other games or platforms. However, how the model trains a Protoss AI agent can show the limitation of DRL and push DRL algorithm a little forward.

This thesis investigates coordinating units through simultaneous coalition structuregeneration and task assignment in a complex Euclidean environment. The environmentused is StarCraft II, and the problem modeled and solved in the game is the distribution ofcombat units over the game’s map. The map was split into regions, and every region wasmodeled as a task to which the combat units were assigned.In a number of experiments, we compare the performance of our approach with thegame’s built-in bots. Against most of the non cheating options, our agent wins 20% of thegames played on a large map, against the Hard built-in bot. On a smaller and simpler mapit wins 22% of games played against the hardest non-cheating difficulty.One of the main limitations of the method used to solve the assignment was the utility function. Which should describe the quality of a coalition and the task assignment.However, as the utility function described the state’s utility better, the win rate increased.Therefore the result indicates that the simultaneous coalition structure generation and taskassignment work for unit distribution in a complex environment like StarCraft II if a sufficient utility function is provided.

The use of computer games has grown exponentially in the past few years and StarCraft II is one of the most popular e-sports today, played by millions of people worldwide. This study investigates the game’s effect on skills, as perceived by the players of StarCraft II. The study was conducted by using an empirical survey and interviews. The skills being investigated were both physical and mental in their nature. The conclusion was that the majority of players perceived that their skills regarding reaction speed, multitasking, APM, analysis and strategy were increased the most, while there was a smaller change in social skills and language skills. Additionally, people who played team games of StarCraft II did, however, feel that their teamwork, conflict management and leadership capabilities increased. / Under de senaste åren har användandet av datorspel vuxit explosionsartat. StarCraft II är ett utav dagens mest populära e-sporter och spelas av miljontals människor runt om i världen. Studien undersöker om spelare uppfattar kompetensförändringar av att spela StarCraft II. Studien utfördes med hjälp utav en enkät och intervjuer. Kompetenserna vi undersökte var av både fysiska och mentala i sin karaktär. Av resultaten kan man se att majoriteten av spelarna uppfattade en ökad kompetens inom reaktionshastighet, multitasking, APM, analytisk- och strategisk förmåga, medan man ser en mindre förändring inom social kompetens och språklig kompetens. Resultaten visar även att de som valde att spela lagspel uppfattade en ökad kompetens inom samarbete, konflikthantering och ledarskap.

The aim of this thesis is to create an automated system for playing a real-time strategy game Starcraft II. Learning from replays via supervised learning and reinforcement learning techniques are used for improving bot's behavior. The proposed system should be capable of playing the whole game utilizing PySC2 framework for machine learning. Performance of the bot is evaluated against the built-in scripted AI in the game.

Machine learning is spearheading progress for the field of artificial intelligence in terms of providing competition in strategy games to a human opponent, be it in a game of chess, Go or poker. A field of machine learning, which shows the most promising results in playing strategy games, is reinforcement learning. The next milestone for the current research lies in a computer game Starcraft II, which outgrows the previous ones in terms of complexity, and represents a potential new breakthrough in this field. The paper focuses on analysis of the problem, and suggests a solution incorporating a reinforcement learning algorithm A2C and hyperparameter optimization implementation PBT, which could mean a step forward for the current progress.