research-article

DotA 2 bots win prediction using naive bayes based on adaboost algorithm

Authors:

Pulung Nurtantio Andono,

Nanang Budi Kurniawan,

Catur SupriyantoAuthors Info & Claims

ICCIP '17: Proceedings of the 3rd International Conference on Communication and Information Processing

Pages 180 - 184

https://doi.org/10.1145/3162957.3162981

Published: 24 November 2017 Publication History

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Abstract

DotA 2 is a multiplayer game that is widely played today. In DotA 2, the players are divided into two teams, i.e., radiant and dire to against each other. Each team consists of five heroes. One hero is played by human and four heroes are controlled by artificial intelligence (AI). In our prediction, we only collect the statistical data of AI heroes. Each team has the main headquarters, which needs to be protected; the headquarters is a called ancient. When the ancient of a team is destroyed, then the game is over. The features of prediction are collected from the statistical value in each bot. Therefore, the player knows which team (radiant or dire) is going to be the winning team. To predict the winning team, we use Naive Bayes (NB) as a classifier in data mining algorithm. NB is the appropriate algorithm. Since NB works on probability, it can predict the winning team, not only the winning heroes. However, NB has a shortcoming, e.g., imbalance data, this study proposes to implement NB+Adaboost. This study evaluates some approaches of NB, i.e., discretization and Gaussian distribution kernel function. Both are used to treat the numerical attribute. The results of the experiment show that the highest accuracy of the win prediction by using NB+Adaboost with Gaussian distribution kernel achieves 80%.

References

[1]

Kinkade, N. and Lim, K. y. K. 2015. DOTA 2 Win Prediction. University of California San Diego.

Google Scholar

[2]

An, Y., Sun, S. and Wang, S. 2017. Naive Bayes Classifiers for Music Emotion Classification Based on Lyrics. in 16th International Conference on Computer and Information Science (ICIS).

Google Scholar

[3]

Goel, A., Gautam, J. and Kumar, S. 2016. Real time sentiment analysis of tweets using Naive Bayes. in 2nd International Conference on Next Generation Computing Technologies.

Google Scholar

[4]

Arifin, D. D., Shaufiah and Bijaksana, M. A. 2016. Enhancing Spam Detection on Mobile Phone Short Message Service (SMS) Performance using FP-Growth and Naive Bayes Classifier. in IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob).

Google Scholar

[5]

Liu, M. 2010. Fingerprint classification based on Adaboost learning from singularity features. Pattern Recognition, vol. 43, pp. 1062--1070.

Digital Library

Google Scholar

[6]

Li, W. and Li, Q. 2010. Using naive Bayes with AdaBoost to enhance network anomaly intrusion detection. in Third International Conference on Intelligent Networks and Intelligent Systems.

Digital Library

Google Scholar

[7]

Huang, K.-Y. and Chang, W.-L. 2010. A Neural Network Method for Prediction of 2006 World Cup Football Game. in The 2010 International Joint Conference on Neural Networks (IJCNN).

Google Scholar

[8]

Shin, J. and Gasparyan, R. 2014. A novel way to Soccer Match Prediction, Stanford University: Department of Computer Science.

Google Scholar

[9]

Prasetio, D. and Harlili. 2016. Predicting Football Match Results with Logistic Regression. in th IGNITE Conference and 2016 International Conference on Advanced Informatics: Concepts, Theory and Application.

Google Scholar

[10]

Cho, H.-C., Kim, K.-J. and Cho, S.-B. 2013. Replay-based strategy prediction and build order adaptation for StarCraft AI bots. in IEEE Conference on Computatonal Intelligence and Games.

Google Scholar

[11]

Zhou, M. and Wei, H. 2009. Constructing weak learner and performance evaluation in AdaBoost. in International Conference on Computational Intelligence and Software Engineering, CiSE.

Google Scholar

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Torres-León JMoreno-Armendáriz MCalvo H(2024)Representing the Information of Multiplayer Online Battle Arena (MOBA) Video Games Using Convolutional Accordion Auto-Encoder (A2E) Enhanced by Attention MechanismsMathematics10.3390/math1217274412:17(2744)Online publication date: 3-Sep-2024
https://doi.org/10.3390/math12172744
Costa LDrachen ASouza FXexéo G(2024)Artificial Intelligence in MOBA Games: A Multivocal Literature MappingIEEE Transactions on Games10.1109/TG.2023.328215716:2(250-269)Online publication date: Jun-2024
https://doi.org/10.1109/TG.2023.3282157
Jusoh SAl Fawareh H(2023)AI Games and Algorithms: An Overview of Categories2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)10.1109/ECAI58194.2023.10194176(1-6)Online publication date: 29-Jun-2023
https://doi.org/10.1109/ECAI58194.2023.10194176
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DotA 2 bots win prediction using naive bayes based on adaboost algorithm

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Information

Published In

ICCIP '17: Proceedings of the 3rd International Conference on Communication and Information Processing

November 2017

545 pages

ISBN:9781450353656

DOI:10.1145/3162957

Conference Chairs:
Jalel Ben-Othman
University of Paris 13, France
,
Feng Gang
UESTC, China
,
Program Chairs:
Jain-Shing Liu
Providence University, Taiwan
,
Masayuki Arai
Graduate School of Science and Engineering Teikyo University, Japan

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 November 2017

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ICCIP 2017

ICCIP 2017: 2017 the 3rd International Conference on Communication and Information Processing

November 24 - 26, 2017

Tokyo, Japan

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Overall Acceptance Rate 61 of 301 submissions, 20%

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Cited By

View all

Torres-León JMoreno-Armendáriz MCalvo H(2024)Representing the Information of Multiplayer Online Battle Arena (MOBA) Video Games Using Convolutional Accordion Auto-Encoder (A2E) Enhanced by Attention MechanismsMathematics10.3390/math1217274412:17(2744)Online publication date: 3-Sep-2024
https://doi.org/10.3390/math12172744
Costa LDrachen ASouza FXexéo G(2024)Artificial Intelligence in MOBA Games: A Multivocal Literature MappingIEEE Transactions on Games10.1109/TG.2023.328215716:2(250-269)Online publication date: Jun-2024
https://doi.org/10.1109/TG.2023.3282157
Jusoh SAl Fawareh H(2023)AI Games and Algorithms: An Overview of Categories2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)10.1109/ECAI58194.2023.10194176(1-6)Online publication date: 29-Jun-2023
https://doi.org/10.1109/ECAI58194.2023.10194176
Jang JWoo JKim H(2022)Action2Score: An Embedding Approach to Score Player ActionProceedings of the ACM on Human-Computer Interaction10.1145/35494836:CHI PLAY(1-23)Online publication date: 31-Oct-2022
https://dl.acm.org/doi/10.1145/3549483
Yue HLiu HChen J(2022) A Gospel for MOBA Game: Ranking-Preserved Hero Change Prediction in Dota 2 IEEE Transactions on Games10.1109/TG.2021.312358314:2(191-201)Online publication date: Jun-2022
https://doi.org/10.1109/TG.2021.3123583
Tian PLan WZhang X(2022)Hero featured learning algorithm for winning rate prediction of Honor of Kings2022 IEEE Conference on Games (CoG)10.1109/CoG51982.2022.9893634(322-329)Online publication date: 21-Aug-2022
https://doi.org/10.1109/CoG51982.2022.9893634
Yu CZhu WSun Y(2019)E-Sports Ban/Pick Prediction Based on Bi-LSTM Meta Learning NetworkArtificial Intelligence and Security10.1007/978-3-030-24274-9_9(97-105)Online publication date: 11-Jul-2019
https://doi.org/10.1007/978-3-030-24274-9_9

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