1,088 research outputs found
ViZDoom Competitions: Playing Doom from Pixels
This paper presents the first two editions of Visual Doom AI Competition,
held in 2016 and 2017. The challenge was to create bots that compete in a
multi-player deathmatch in a first-person shooter (FPS) game, Doom. The bots
had to make their decisions based solely on visual information, i.e., a raw
screen buffer. To play well, the bots needed to understand their surroundings,
navigate, explore, and handle the opponents at the same time. These aspects,
together with the competitive multi-agent aspect of the game, make the
competition a unique platform for evaluating the state of the art reinforcement
learning algorithms. The paper discusses the rules, solutions, results, and
statistics that give insight into the agents' behaviors. Best-performing agents
are described in more detail. The results of the competition lead to the
conclusion that, although reinforcement learning can produce capable Doom bots,
they still are not yet able to successfully compete against humans in this
game. The paper also revisits the ViZDoom environment, which is a flexible,
easy to use, and efficient 3D platform for research for vision-based
reinforcement learning, based on a well-recognized first-person perspective
game Doom
Proceedings of Mathsport international 2017 conference
Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017.
MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet.
Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports
Xavier University Newswire
https://www.exhibit.xavier.edu/student_newspaper/3792/thumbnail.jp
2019-20 Ole Miss Rifle Fact Book
https://egrove.olemiss.edu/med_rifle/1009/thumbnail.jp
Spartan Daily, October 7, 1975
Volume 65, Issue 17https://scholarworks.sjsu.edu/spartandaily/6001/thumbnail.jp
Xavier University Newswire
https://www.exhibit.xavier.edu/student_newspaper/1635/thumbnail.jp
Football analytics: a literature analysis from 2010 to 2020
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe overall goal for the current study is to present a literature review of analytics, precisely machine
learning (ML) reference authors in terms of methods and applicable scopes of study, in football
where is a field that historically there are empirical decisions and the usage of analytics has been
growing intensely. The research aims to list relevant academic contributions published between 2010
and 2020, performing a comparable picture per authors across the following subsets: player
individual technical skills and team performance. Furthermore, the approach will provide a summary
of studies for machine learning methods applied in football.
Such outcomes of this study would contribute to the discussion about football analytics. Regarding
that these summaries can drive researchers to have a deep dive into the fields of interest straight to
references preview studied in the thesis. Results indicate that football analytics has broadly vast
opportunities in terms of research, regarding machine learning methods and a high potential to have
a deep exploration of team and player perspective. This study can leverage and pavement new
further in-depth and targeted investigation toward football analytics
Cash Box, July 25, 1964
The international music record weeklyPublication ceased with Nov. 199
The Chronicle [March 22, 2001]
The Chronicle, March 22, 2001https://repository.stcloudstate.edu/chron/4484/thumbnail.jp
The collection, analysis and exploitation of footballer attributes: A systematic review
© 2022 – The authors. Published by IOS Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution-Non Commercial License (CC BY-NC 4.0)There is growing on-going research into how footballer attributes, collected prior to, during and post-match, may address the demands of clubs, media pundits and gaming developers. Focusing upon individual player performance analysis and prediction, we examined the body of research which considers different player attributes. This resulted in the selection of 132 relevant papers published between 1999 and 2020. From these we have compiled a comprehensive list of player attributes, categorising them as static, such as age and height, or dynamic, such as pass completions and shots on target. To indicate their accuracy, we classified each attribute as objectively or subjectively derived, and finally by their implied accessibility and their likely personal and club sensitivity. We assigned these attributes to 25 logical groups such as passing, tackling and player demographics. We analysed the relative research focus on each group and noted the analytical methods deployed, identifying which statistical or machine learning techniques were used. We reviewed and considered the use of character trait attributes in the selected papers and discuss more formal approaches to their use. Based upon this we have made recommendations on how this work may be developed to support elite clubs in the consideration of transfer targets.Peer reviewedFinal Published versio
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