Utilization of the Particle Swam Optimization Algorithm in Game Dota 2
DOI:
https://doi.org/10.26594/register.v10i2.3503Keywords:
MOBA, Dota 2, Artificial Intelligence, Particle Swarm OprimizationAbstract
Dota 2, a Multiplayer Online Battle Arena game, is widely popular among gamers, with many attempting to create efficient artificial intelligence that can play like a human. However, current AI technology still falls short in some areas, despite some AI models being able to play decently. To address this issue, researchers continue to explore ways to enhance AI performance in Dota 2. This study focuses on the process of developing artificial intelligence code in Dota 2 and integrating the particle swarm optimization algorithm into Dota 2 Team's Desire. Although particle swarm optimization is an old evolutionary algorithm, it is still considered effective in achieving optimal solutions. The study found that PSO significantly improved the AI Team's Desire and enabled it to win against Default AI of similar levels or players with low MMR. However, it was still unable to defeat opponents with higher AI levels. Furthermore, this study is expected to assist other researchers in developing artificial intelligence in Dota 2, as the complexity of the development process lies not only in AI but also in language, structure, and communication between files.
References
Z. Hellman and M. Pintér, “Charges and bets: a general characterisation of common priors,” Int. J. Game Theory, vol. 51, no. 3, pp. 567–587, 2022, doi: 10.1007/s00182-022-00805-4.
T. Hazra and K. Anjaria, “Applications of game theory in deep learning: a survey,” Multimed. Tools Appl., vol. 81, no. 6, pp. 8963–8994, 2022, doi: 10.1007/s11042-022-12153-2.
E. R. Sadik-Zada, A. Gatto, L. Aldieri, G. Bimonte, L. Senatore, and C. P. Vinci, “Game Theory Applications to Socio-Environmental Studies, Development Economics, and Sustainability Research,” Games, vol. 15, no. 1. 2024, doi: 10.3390/g15010005.
D. Bulut, Y. Samur, and Z. Cömert, “The effect of educational game design process on students’ creativity,” Smart Learn. Environ., vol. 9, no. 1, p. 8, 2022, doi: 10.1186/s40561-022-00188-9.
Y. Gui, Z. Cai, Y. Yang, L. Kong, X. Fan, and R. H. Tai, “Effectiveness of digital educational game and game design in STEM learning: a meta-analytic review,” Int. J. STEM Educ., vol. 10, no. 1, p. 36, 2023, doi: 10.1186/s40594-023-00424-9.
P. W. Atmaja, D. O. Siahaan, and I. Kuswardayan, “Game Design Document Format For Video Games With Passive Dynamic Difficulty Adjustment,” Regist. J. Ilm. Teknol. Sist. Inf., vol. 2, no. 2 SE-Article, pp. 86–97, Jul. 2016, doi: 10.26594/register.v2i2.551.
B. Xia, X. Ye, and A. Abuassba, Recent Research on AI in Games. 2020.
N. A. Barriga, M. Stanescu, F. Besoain, and M. Buro, “Improving RTS Game AI by Supervised Policy Learning, Tactical Search, and Deep Reinforcement Learning,” IEEE Comput. Intell. Mag., vol. 14, no. 3, pp. 8–18, 2019, doi: 10.1109/MCI.2019.2919363.
S. Karim, “Perubahan perilaku Non-Player Character (NPC) pada Game Arabic Hunter menggunakan Jaringan Syaraf Tiruan Perceptron,” Regist. J. Ilm. Teknol. Sist. Inf., vol. 3, no. 1 SE-Article, pp. 34–41, Jan. 2017, doi: 10.26594/register.v3i1.622.
H. Armanto, H. A. Rosyid, Muladi, and Gunawan, “Improved Non-Player Character (NPC) behavior using evolutionary algorithm—A systematic review,” Entertain. Comput., vol. 52, p. 100875, 2025, doi: https://doi.org/10.1016/j.entcom.2024.100875.
M. Mora-Cantallops and M.-Á. Sicilia, “MOBA games: A literature review,” Entertain. Comput., vol. 26, Feb. 2018, doi: 10.1016/j.entcom.2018.02.005.
G. Robertson and I. Watson, “A Review of Real-Time Strategy Game AI,” AI Mag., vol. 35, no. 4, pp. 75–104, 2014, doi: 10.1609/aimag.v35i4.2478.
D. Mauleon, Online Battle Arena Esports: The Competitive Gaming World of League of Legends, Dota 2, and More! Capstone, 2019.
E. F. Fangasadha, S. Soeroredjo, Anderies, and A. A. S. Gunawan, “Literature Review of OpenAI Five’s Mechanisms in Dota 2’s Bot Player,” in 2022 International Seminar on Application for Technology of Information and Communication (iSemantic), 2022, pp. 183–190, doi: 10.1109/iSemantic55962.2022.9920480.
C. Berner et al., “Dota 2 with large scale deep reinforcement learning,” arXiv Prepr. arXiv1912.06680, 2019.
A. Semenov, P. Romov, S. Korolev, D. Yashkov, and K. Neklyudov, “Performance of Machine Learning Algorithms in Predicting Game Outcome from Drafts in Dota 2,” 2016.
C. H. Ke et al., “DOTA 2 match prediction through deep learning team fight models,” in 2022 IEEE Conference on Games (CoG), 2022, pp. 96–103, doi: 10.1109/CoG51982.2022.9893647.
D. Freitas, L. G. Lopes, and F. Morgado-Dias, “Particle Swarm Optimisation: A Historical Review Up to the Current Developments,” Entropy, vol. 22, no. 3. 2020, doi: 10.3390/e22030362.
Q. Xiong, X. Zhang, X. Xu, and S. He, “A Modified Chaotic Binary Particle Swarm Optimization Scheme and Its Application in Face-Iris Multimodal Biometric Identification,” Electronics, vol. 10, no. 2. 2021, doi: 10.3390/electronics10020217.
S. Pervaiz, Z. Ul-Qayyum, W. H. Bangyal, L. Gao, and J. Ahmad, “A Systematic Literature Review on Particle Swarm Optimization Techniques for Medical Diseases Detection,” Comput. Math. Methods Med., vol. 2021, p. 5990999, 2021, doi: 10.1155/2021/5990999.
Y. Yang, Q. Liao, J. Wang, and Y. Wang, “Application of multi-objective particle swarm optimization based on short-term memory and K-means clustering in multi-modal multi-objective optimization,” Eng. Appl. Artif. Intell., vol. 112, p. 104866, 2022, doi: https://doi.org/10.1016/j.engappai.2022.104866.
C. Zhang, H. Shao, and Y. Li, “Particle swarm optimisation for evolving artificial neural network,” in Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. “cybernetics evolving to systems, humans, organizations, and their complex interactions” (cat. no.0, 2000, vol. 4, pp. 2487–2490 vol.4, doi: 10.1109/ICSMC.2000.884366.
S. Mirjalili, J. Song Dong, A. Lewis, and A. S. Sadiq, “Particle Swarm Optimization: Theory, Literature Review, and Application in Airfoil Design BT - Nature-Inspired Optimizers: Theories, Literature Reviews and Applications,” S. Mirjalili, J. Song Dong, and A. Lewis, Eds. Cham: Springer International Publishing, 2020, pp. 167–184.
H. Armanto, H. A. Rosyid, and others, “Evolutionary Algorithm in Game--A Systematic Review,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, 2023.
I. Media, A Beginner’s Guide to DOTA 2. CreateSpace Independent Publishing Platform, 2014.
R. Ierusalimschy, Programming in Lua. Roberto Ierusalimschy, 2006.
J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95 - International Conference on Neural Networks, 1995, vol. 4, pp. 1942–1948 vol.4, doi: 10.1109/ICNN.1995.488968.
Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” in 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), 1998, pp. 69–73, doi: 10.1109/ICEC.1998.699146.
R. C. Eberhart and Y. Shi, “Comparing inertia weights and constriction factors in particle swarm optimization,” in Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512), 2000, vol. 1, pp. 84–88 vol.1, doi: 10.1109/CEC.2000.870279.
M.-P. Song and G.-C. Gu, “Research on particle swarm optimization: a review,” in Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826), 2004, vol. 4, pp. 2236–2241 vol.4, doi: 10.1109/ICMLC.2004.1382171.
S. Sendari, A. N. Afandi, I. A. E. Zaeni, Y. D. Mahandi, K. Hirasawa, and H.-I. Lin, “Exploration of genetic network programming with two-stage reinforcement learning for mobile robot,” TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 17, no. 3, pp. 1447–1454, 2019.
D. Lestari, S. Sendari, and I. A. E. Zaeni, “Genetic algorithm for finding shortest path of mobile robot in various static environments,” J. Infotel, vol. 15, no. 3, pp. 280–286, 2023.
S. Sendari, A. B. Putra Utama, N. S. Fanany Putri, P. Widiharso, and R. J. Putra, “K-Means and Fuzzy C-Means Optimization using Genetic Algorithm for Clustering Questions ,” Int. J. Adv. Sci. Comput. Appl., vol. 1, no. 1 SE-Articles, pp. 1–9, Dec. 2021, doi: 10.47679/ijasca.v1i1.2.
M. Wu, S. Xiong, and H. Iida, Fairness Mechanism in Multiplayer Online Battle Arena Games. 2016.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Hendrawan Armanto, Harits Ar Rosyid, Muladi Muladi, Gunawan Gunawan
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Please find the rights and licenses in Register: Jurnal Ilmiah Teknologi Sistem Informasi. By submitting the article/manuscript of the article, the author(s) agree with this policy. No specific document sign-off is required.
1. License
The non-commercial use of the article will be governed by the Creative Commons Attribution license as currently displayed on Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
2. Author(s)' Warranties
The author warrants that the article is original, written by stated author(s), has not been published before, contains no unlawful statements, does not infringe the rights of others, is subject to copyright that is vested exclusively in the author and free of any third party rights, and that any necessary written permissions to quote from other sources have been obtained by the author(s).
3. User/Public Rights
Register's spirit is to disseminate articles published are as free as possible. Under the Creative Commons license, Register permits users to copy, distribute, display, and perform the work for non-commercial purposes only. Users will also need to attribute authors and Register on distributing works in the journal and other media of publications. Unless otherwise stated, the authors are public entities as soon as their articles got published.
4. Rights of Authors
Authors retain all their rights to the published works, such as (but not limited to) the following rights;
Copyright and other proprietary rights relating to the article, such as patent rights,
The right to use the substance of the article in own future works, including lectures and books,
The right to reproduce the article for own purposes,
The right to self-archive the article (please read out deposit policy),
The right to enter into separate, additional contractual arrangements for the non-exclusive distribution of the article's published version (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal (Register: Jurnal Ilmiah Teknologi Sistem Informasi).
5. Co-Authorship
If the article was jointly prepared by more than one author, any authors submitting the manuscript warrants that he/she has been authorized by all co-authors to be agreed on this copyright and license notice (agreement) on their behalf, and agrees to inform his/her co-authors of the terms of this policy. Register will not be held liable for anything that may arise due to the author(s) internal dispute. Register will only communicate with the corresponding author.
6. Royalties
Being an open accessed journal and disseminating articles for free under the Creative Commons license term mentioned, author(s) aware that Register entitles the author(s) to no royalties or other fees.
7. Miscellaneous
Register will publish the article (or have it published) in the journal if the article’s editorial process is successfully completed. Register's editors may modify the article to a style of punctuation, spelling, capitalization, referencing and usage that deems appropriate. The author acknowledges that the article may be published so that it will be publicly accessible and such access will be free of charge for the readers as mentioned in point 3.
Editorial Board
Author Guidelines
Focus and Scope
Online Submissions
Author Fees 
Journal History
