746 research outputs found
ASPIRE Adaptive strategy prediction in a RTS environment
When playing a Real Time Strategy(RTS) game against the non-human player(bot) it is important that the bot can do different strategies to create a challenging experience over time. In this thesis we aim to improve the way the bot can predict what strategies the player is doing by analyzing the replays of the given players games. This way the bot can change its strategy based upon the known knowledge of the game state and what strategies the player have used before. We constructed a Bayesian Network to handle the predictions of the opponent's strategy and inserted that into a preexisting bot. Based on the results from our experiments we can state that the Bayesian Network adapted to the strategies our bot was exposed to. In addition we can see that the Bayesian Network only predicted the possible strategies given the obtained information about the game state.INFO390MASV-INF
The Principles of Esports Engagement: A Universal Code of Conduct
Section I of this article provides a brief background of esports and the ESA. Section II states the four principles of esports engagement announced by the ESA. Section III applies these four principles by reviewing specific problems that have plagued the video game and esports industries, such as toxicity (especially towards women and other minorities), swatting, cheating, and other malicious behavior. This article concludes by discussing implementation of a universal code of conduct in esports based on the principles of esports engagement
Player Behavior Modeling In Video Games
Player Behavior Modeling in Video Games In this research, we study players’ interactions in video games to understand player behavior. The first part of the research concerns predicting the winner of a game, which we apply to StarCraft and Destiny. We manage to build models for these games which have reasonable to high accuracy. We also investigate which features of a game comprise strong predictors, which are economic features and micro commands for StarCraft, and key shooter performance metrics for Destiny, though features differ between different match types. The second part of the research concerns distinguishing playing styles of players of StarCraft and Destiny. We find that we can indeed recognize different styles of playing in these games, related to different match types. We relate these different playing styles to chance of winning, but find that there are no significant differences between the effects of different playing styles on winning. However, they do have an effect on the length of matches. In Destiny, we also investigate what player types are distinguished when we use Archetype Analysis on playing style features related to change in performance, and find that the archetypes correspond to different ways of learning. In the final part of the research, we investigate to what extent playing styles are related to different demographics, in particular to national cultures. We investigate this for four popular Massively multiplayer online games, namely Battlefield 4, Counter-Strike, Dota 2, and Destiny. We found that playing styles have relationship with nationality and cultural dimensions, and that there are clear similarities between the playing styles of similar cultures. In particular, the Hofstede dimension Individualism explained most of the variance in playing styles between national cultures for the games that we examined
ESTA: An Esports Trajectory and Action Dataset
Sports, due to their global reach and impact-rich prediction tasks, are an
exciting domain to deploy machine learning models. However, data from
conventional sports is often unsuitable for research use due to its size,
veracity, and accessibility. To address these issues, we turn to esports, a
growing domain that encompasses video games played in a capacity similar to
conventional sports. Since esports data is acquired through server logs rather
than peripheral sensors, esports provides a unique opportunity to obtain a
massive collection of clean and detailed spatiotemporal data, similar to those
collected in conventional sports. To parse esports data, we develop awpy, an
open-source esports game log parsing library that can extract player
trajectories and actions from game logs. Using awpy, we parse 8.6m actions,
7.9m game frames, and 417k trajectories from 1,558 game logs from professional
Counter-Strike tournaments to create the Esports Trajectory and Actions (ESTA)
dataset. ESTA is one of the largest and most granular publicly available sports
data sets to date. We use ESTA to develop benchmarks for win prediction using
player-specific information. The ESTA data is available at
https://github.com/pnxenopoulos/esta and awpy is made public through PyPI
Applied Machine Learning for Games: A Graduate School Course
The game industry is moving into an era where old-style game engines are
being replaced by re-engineered systems with embedded machine learning
technologies for the operation, analysis and understanding of game play. In
this paper, we describe our machine learning course designed for graduate
students interested in applying recent advances of deep learning and
reinforcement learning towards gaming. This course serves as a bridge to foster
interdisciplinary collaboration among graduate schools and does not require
prior experience designing or building games. Graduate students enrolled in
this course apply different fields of machine learning techniques such as
computer vision, natural language processing, computer graphics, human computer
interaction, robotics and data analysis to solve open challenges in gaming.
Student projects cover use-cases such as training AI-bots in gaming benchmark
environments and competitions, understanding human decision patterns in gaming,
and creating intelligent non-playable characters or environments to foster
engaging gameplay. Projects demos can help students open doors for an industry
career, aim for publications, or lay the foundations of a future product. Our
students gained hands-on experience in applying state of the art machine
learning techniques to solve real-life problems in gaming.Comment: The Eleventh Symposium on Educational Advances in Artificial
Intelligence (EAAI-21
Business Models In E-Sports: Starcraft 2
E-sports, or electronic sports, is a term referring to competitive (video)gaming, where players face off against each other in serious matches and tournaments. While e-sports have become one of the major forms of digital culture and form of business in gaming, research within e-sports is yet scarce. This exploratory study aims to further the understanding of the business ecosystem surrounding e-sports. We document and investigate different actors, players, their relationship and revenue models in one of the world’s biggest e-sports ecosystems around the game Starcraft 2. We employ the e3-value methodology, along with a qualitative analysis, to build an understanding of the e-sports ecosystem. Through this ecology analysis, five distinct revenue models are identified and the key actors of these are presented. Based on our results, e-sport players employ tournament earnings, casting, coaching, team salary and sponsorships as their main revenue models. Furthermore, the study illustrates the vital importance of sponsors to the ecosystem
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