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A Methodology for Player Modeling based on Machine Learning [article]

Marlos C. Machado
2013 arXiv   pre-print
We conclude our work using the learned models to infer human players' preferences. Using some of the evaluated classifiers we obtained accuracies over 60% for most of the inferred preferences.  ...  We compared four different methods, based on different paradigms (SVM, AdaBoost, NaiveBayes and JRip), evaluating them on a set of matches played by different virtual agents.  ...  Acknowledgments The authors survey the field, classifying each work in one of four categories: Player Action Modeling, Player Tactics Modeling, Player Strategies Modeling and Player Profiling.  ... 
arXiv:1312.3903v1 fatcat:dshyw7sdavef3nwatus7ympag4

Learning-Based Video Game Development in MLP@UoM: An Overview [article]

Ke Chen
2019 arXiv   pre-print
Unlike traditional methodologies, in Machine Learning and Perception Lab at the University of Manchester (MLP@UoM), we advocate applying machine learning to different tasks in video game development to  ...  In general, video games not only prevail in entertainment but also have become an alternative methodology for knowledge learning, skill acquisition and assistance for medical treatment as well as health  ...  ACKNOWLEDGMENT The work presented in this paper was collaborated with those MLP@UoM members who worked in learning-based video game development.  ... 
arXiv:1908.10127v1 fatcat:ocsk6by7c5ap5fgzi64ydxsnby

Cricket Player Selection using Machine Learning

2020 International Journal of Engineering and Advanced Technology  
This paper suggests an important approach for Selecting Cricket players by Evaluating his Statistics and Provides a comparative look at machine learning techniques in cricket player selection.  ...  In this paper a model for Bowlers and Batsmen Separately was proposed which was implemented using Random Forest, AdaBoost, Support Vector Machines(SVM), LightGBM,CatBoost, Logistic Regression Linear Discriminant  ...  The main objectives of this paper: 1.To build a machine learning model for ipl franchise and BCCI selection Committee to predict the player Selection based on simple parameters -Not outs,, player Name,  ... 
doi:10.35940/ijeat.e9291.069520 fatcat:m5pi6xluavf5pcfvfeudig2cji

Use of Machine Learning to Automate the Identification of Basketball Strategies Using Whole Team Player Tracking Data

Tian, De Silva, Caine, Swanson
2019 Applied Sciences  
A classification model is developed based on a player and ball tracking dataset from National Basketball Association (NBA) game play to classify the adopted defensive strategy against pick-and-roll play  ...  Machine learning techniques, such as the one adopted here, have the potential to enable a deeper understanding of player decision making and defensive game strategies in basketball and other sports, by  ...  several machine learning models as candidates for classification.  ... 
doi:10.3390/app10010024 fatcat:lupgptvlv5fbbiryff2hxlhstm

Predicting the Best Team Players of Pakistan Super League using Machine Learning Algorithms

Jawaria Ashraf, Sania Bhatti, Shahnawaz Talpur
2021 International Journal of Computer Applications  
of PSL using Machine learning approach.  ...  However, the selection of the best players for PSL teams is a very critical phase which certainly affects the final results of the play.  ...  Therefore, this study focuses on the prediction of the final results based on the selection of the players using different machine learning approaches before match.  ... 
doi:10.5120/ijca2021921486 fatcat:er3dvhbd65b43fhjkbo4mt54uy

Machine learning methods in sport injury prediction and prevention: a systematic review

Hans Van Eetvelde, Luciana D Mendonça, Christophe Ley, Romain Seil, Thomas Tischer
2021 Journal of Experimental Orthopaedics  
Machine learning (ML) methods could be used to improve injury prediction and allow proper approaches to injury prevention.  ...  The aim of our study was therefore to perform a systematic review of ML methods in sport injury prediction and prevention. A search of the PubMed database was performed on March 24th 2020.  ...  In terms of ML methodology, the following observations can be made from this review. (i) Tree-based models are currently the most popular ML models in sports medicine.  ... 
doi:10.1186/s40634-021-00346-x pmid:33855647 fatcat:2gxusp3vhvbplfg5ivz4bwv6eu

Two-phased DEA-MLA approach for predicting efficiency of NBA players

Sandro Radovanovic, Milan Radojicic, Gordana Savic
2014 Yugoslav Journal of Operations Research  
Therefore, to predict the efficiency of a new player, machine learning algorithms are applied.  ...  The efficiency is evaluated for 26 NBA players at the guard position based on existing data.  ...  The objective is to learn an unknown function based on a training set of N input-output pairs in a black box modelling approach.  ... 
doi:10.2298/yjor140430030r fatcat:kxle24iocbao5kruetv6qacbvi


Jake Bowers
2014 Sociological methodology  
Future methodology building on simple QCA might adapt insights from machine learning to overcome current shortcomings or advise against the use of QCA for particular designs and data.  ...  game is not limited to supervised linear model-based algorithms.  ...  The adaptive lasso penalty is p(l, b p , w p ) = lw p |b p |, where w p = 1=b p andb p arises from a previous linear model (here a ridge regression but often an ordinary least squares regression).  ... 
doi:10.1177/0081175014542078 fatcat:aeolkhbv2jhkdd53ppucjukvii

Augmented Q Imitation Learning (AQIL) [article]

Xiao Lei Zhang, Anish Agarwal
2020 arXiv   pre-print
Traditional deep reinforcement learning takes a significant time before the machine starts to converge to an optimal policy.  ...  In imitation learning the machine learns by mimicking the behavior of an expert system whereas in reinforcement learning the machine learns via direct environment feedback.  ...  During the imitation learning process, the Q-learning model optimizes the reward based on following the expert input.  ... 
arXiv:2004.00993v2 fatcat:nwtzjuijwfgqfd3paiu34iy7my

Learning Personalized Models of Human Behavior in Chess [article]

Reid McIlroy-Young, Russell Wang, Siddhartha Sen, Jon Kleinberg, Ashton Anderson
2021 arXiv   pre-print
Even when machine learning systems surpass human ability in a domain, there are many reasons why AI systems that capture human-like behavior would be desirable: humans may want to learn from them, they  ...  Chess is a rich domain for exploring these questions, since it combines a set of appealing features: AI systems have achieved superhuman performance but still interact closely with human chess players  ...  The end result is a transfer learning methodology for creating a personalized model for any player, given a sufficient number of the player's games.  ... 
arXiv:2008.10086v2 fatcat:p52f5u2qgzb7lcomfxrcs6tabu

Intelligent Cricket Team Selection by Predicting Individual Players' Performance using Efficient Machine Learning Technique

2020 International Journal of Engineering and Advanced Technology  
Players are selected based on different criteria.  ...  Researchers' uses machine learning approach for prediction.  ...  Passi and Pandey [5] modeled records for hitting and bowling based on player statistics and characteristics.  ... 
doi:10.35940/ijeat.c6339.029320 fatcat:ntaqwtl67jfbzegolinnteszvi

Remuneration and Tariffs in the Context of Virtual Power Players [chapter]

Catarina Ribeiro, Tiago Pinto, Zita Vale, José Baptista
2017 Advances in Intelligent Systems and Computing  
Virtual Power Players (VPPs) are a new player type which allows aggregating a diversity of players (distribution Generation, storage units, electrical vehicles, and consumers) to participate in the markets  ...  This requires new business models able to cope with the new opportunities.  ...  The players modelling considers the operation and market context. The terms for new contracts and best strategies for each context are determined by means of machine learning based methods.  ... 
doi:10.1007/978-3-319-61578-3_39 fatcat:ny42xga3orbgjov74swgivrtga

Understanding Cyber Athletes Behaviour Through a Smart Chair: CS:GO and Monolith Team Scenario [article]

Anton Smerdov and Anastasia Kiskun and Rostislav Shaniiazov and Andrey Somov and Evgeny Burnaev
2019 arXiv   pre-print
In this work, we propose a smart chair platform - an unobtrusive approach to the collection of data on the eSports athletes and data further processing with machine learning methods.  ...  In particular, we collect data from the accelerometer and gyroscope integrated in the chair and apply machine learning algorithms for the data analysis.  ...  Also, the authors thank Alexey "ub1que" Polivanov for supporting the experiments by providing a slot at the CS:GO Online Retake server (  ... 
arXiv:1908.06407v1 fatcat:jpe7gd7llbcbpcy35zanooyrxm

Understanding gender differences in professional European football through machine learning interpretability and match actions data

Marc Garnica-Caparrós, Daniel Memmert
2021 Scientific Reports  
A methodology for unbiased feature extraction and objective analysis is presented based on data integration and machine learning explainability algorithms.  ...  Each model tried to draw the differences between male and female data points, and we extracted the results using machine learning explainability methods to understand the underlying mechanics of the models  ...  Acknowledgements This study was possible thanks to a data collaboration with the Leuphana University of Lünenburg (Prof. Dr. Ulf Brefeld).  ... 
doi:10.1038/s41598-021-90264-w pmid:34031518 fatcat:22fa3h6fhbftfnd7rsjtu7hxmy

Developing Edu-Game "Ulun Smart-Kid" Learning Media of Banjar Language and Game Agent with Finite State Machine Model

Reza Andrea, Software Engineering Technology, Samarinda State Agricultural Polytechnic, 75131, Indonesia, Asep Nurhuda, STMIK Widya Cipta Dharma, Samarinda, 75123, Indonesia
2020 International Journal of Education and Management Engineering  
Using the finite state machine model method game that is built will have game agent character that will accompany a child to play like a teacher.  ...  In this game player must arrange letters in random and create a word in Banjar language. AI technology (artificial intelligence) will also be applied to this research.  ...  Assembly Finite State Machine The FSM model ( figure 6 ) is applied as a funny character expression pattern on the game, the funny character will give notification and speak if the player is wrong or  ... 
doi:10.5815/ijeme.2020.05.02 fatcat:v34vxrsga5fq5fyp7i7g54ewiq
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