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Learning from Games and Contests

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: 20 July 2024 | Viewed by 5440

Special Issue Editor


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Guest Editor
Department of Electrical and Computer Engineering faculty, Brigham Young University, Provo, UT 84602, USA
Interests: physical-layer security; information theory; probability and statistics; sports analytics; machine learning; digital telecommunications

Special Issue Information

Dear Colleagues,

Games, contests, puzzles, etc. form an integral part of many peoples’ lives around the world. Advances in technology are also often linked to the analysis of these forms of entertainment. For example, some breakthroughs in AI have been made in conjunction with the development of IBM’s Watson and its ability to play the gameshow Jeopardy. Another more recent example is that of DeepMind’s AlphaGo and its learned ability to defeat world-champion Go players. In these cases and others, media attention to the areas of probability, statistics, machine learning, information theory, etc. has surpassed attention given for other advances of a similar depth that have more technical applications. In other words, technical fields gain significant notoriety when advances are coupled with applications that hold interest for the general public.

This Special Issue calls for the submission of contributions that apply the tools of information theory, probability, statistics, machine learning, game theory, graph theory, artificial intelligence, etc. to applications that involve the playing of games. Papers that analyze games from a technical point of view are welcome. Contributions that seek to give humans a competitive advantage during contests are also appropriate. Papers must exhibit significant technical content related to the scope of Entropy; that is, papers that take an information-theoretic approach using entropy, mutual information, etc. are of greatest value to this Special Issue. Applications of interest include games such as chess and Go, online games such as Wordle, and contests such as those found in the areas of sports, politics, etc. We also welcome other creative applications in this domain.

This Special Issue further seeks to provide a mechanism by which creative works in information theory for nonconventional applications might be published in a meaningful venue. Papers published in this Special Issue will help us learn from the games we play.

Dr. Willie Harrison
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • information theory
  • probability and statistics
  • machine learning and artificial intelligence
  • game theory
  • graph theory and combinatorics
  • game play
  • sports analytics
  • contest analysis

Published Papers (2 papers)

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Research

22 pages, 1209 KiB  
Article
On the Value of Chess Squares
by Aditya Gupta, Shiva Maharaj, Nicholas Polson and Vadim Sokolov
Entropy 2023, 25(10), 1374; https://doi.org/10.3390/e25101374 - 24 Sep 2023
Viewed by 1722
Abstract
We propose a neural network-based approach to calculate the value of a chess square–piece combination. Our model takes a triplet (color, piece, square) as the input and calculates a value that measures the advantage/disadvantage of having this piece on this square. Our methods [...] Read more.
We propose a neural network-based approach to calculate the value of a chess square–piece combination. Our model takes a triplet (color, piece, square) as the input and calculates a value that measures the advantage/disadvantage of having this piece on this square. Our methods build on recent advances in chess AI, and can accurately assess the worth of positions in a game of chess. The conventional approach assigns fixed values to pieces (= , = 9, = 5, = 3, = 3, = 1). We enhance this analysis by introducing marginal valuations. We use deep Q-learning to estimate the parameters of our model. We demonstrate our method by examining the positioning of knights and bishops, and also provide valuable insights into the valuation of pawns. Finally, we conclude by suggesting potential avenues for future research. Full article
(This article belongs to the Special Issue Learning from Games and Contests)
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16 pages, 3036 KiB  
Article
Enhancing Basketball Game Outcome Prediction through Fused Graph Convolutional Networks and Random Forest Algorithm
by Kai Zhao, Chunjie Du and Guangxin Tan
Entropy 2023, 25(5), 765; https://doi.org/10.3390/e25050765 - 8 May 2023
Cited by 3 | Viewed by 3084
Abstract
Basketball is a popular sport worldwide, and many researchers have utilized various machine learning models to predict the outcome of basketball games. However, prior research has primarily focused on traditional machine learning models. Furthermore, models that rely on vector inputs tend to ignore [...] Read more.
Basketball is a popular sport worldwide, and many researchers have utilized various machine learning models to predict the outcome of basketball games. However, prior research has primarily focused on traditional machine learning models. Furthermore, models that rely on vector inputs tend to ignore the intricate interactions between teams and the spatial structure of the league. Therefore, this study aimed to apply graph neural networks to basketball game outcome prediction, by transforming structured data into unstructured graphs, to represent the interactions between teams in the 2012–2018 NBA season dataset. Initially, the study used a homogeneous network and undirected graph to build a team representation graph. The constructed graph was fed into a graph convolutional network, which yielded an average success rate of 66.90% in predicting the outcome of games. To improve the prediction success rate, feature extraction based on the random forest algorithm was combined with the model. The fused model yielded the best results, and the prediction accuracy was improved to 71.54%. Additionally, the study compared the results of the developed model with previous studies and the baseline model. Our proposed method considers the spatial structure of teams and the interaction between teams, resulting in superior performance in basketball game outcome prediction. The results of this study provide valuable insights for basketball performance prediction research. Full article
(This article belongs to the Special Issue Learning from Games and Contests)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Decision-Theoretic Entropy
Author: Pierfrancesco La Mura
Affiliation: HHL Leipzig Graduate School of Management, Leipzig, Germany
Abstract: We introduce an axiomatic approach to the problem of inferring a complete and transitive weak ordering representing the agent's preferences given a set of observed constraints. The axioms characterize a unique inference rule, which amounts to the constrained maximization of a certain formula we derive. The formula can be interpreted as the entropy of the agent's preference ordering, and its unique maximand identifies the simplest rationalization of the observed behavior.
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