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Applied Sciences
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Applications of Artificial Intelligence and Machine Learning in Games
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Applications of Artificial Intelligence and Machine Learning in Games

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 June 2024 | Viewed by 14762

Special Issue Editors

Dr. Maxim Mozgovoy

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, The University of Aizu, Tsuruga, Ikki-machi, Aizu-Wakamatsu 965-8580, Japan
Interests: AI for computer games; computer-assised language learning; software engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Paolo Burelli

E-Mail Website
Guest Editor
Digital Design Department, IT University of Copenhagen, Rued Langgaards Vej 7, DK-2300 Copenhagen S, Denmark
Interests: game AI; playtesting agents; player modelling; user modelling; player emotions and cognition; adaptive systems

Special Issue Information

Dear Colleagues,

Since the early years of computing, games have been used as testing environments for new methods and technologies of artificial intelligence (AI). The study of game worlds from checkers and chess to Go and StarCraft greatly contributed to the present achievements of AI research. Games also set new challenges for AI systems, requiring them to be skillful and adaptable opponents, believable neutral characters, or smart and helpful teammates. The proposed Special Issue of Applied Sciences aims to provide a venue for discussing all current topics of game AI research. We invite works reporting original research results, as well as review and opinion papers.

Dr. Maxim Mozgovoy
Dr. Paolo Burelli
Guest Editors

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • game AI
  • machine learning
  • multi-agent systems
  • player modeling
  • serious games
  • gamification
  • procedural content generation
  • behavior construction
  • automated playtesting

Published Papers (5 papers)

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Research

17 pages, 1427 KiB  
Article
Machine Learning in Gamification and Gamification in Machine Learning: A Systematic Literature Mapping
by Jakub Swacha and Michał Gracel
Appl. Sci. 2023, 13(20), 11427; https://doi.org/10.3390/app132011427 - 18 Oct 2023
Viewed by 1312
Abstract
Albeit in different ways, both machine learning and gamification have transfigured the user experience of information systems. Although both are hot research topics, so far, little attention has been paid to how these two technologies converge with each other. This relation is not [...] Read more.
Albeit in different ways, both machine learning and gamification have transfigured the user experience of information systems. Although both are hot research topics, so far, little attention has been paid to how these two technologies converge with each other. This relation is not obvious as while it is feasible to enhance gamification with machine learning, it is also feasible to support machine learning with gamification; moreover, there are applications in which machine learning and gamification are combined yet not directly connected. In this study, we aim to shed light on the use of both machine learning in gamification and gamification in machine learning, as well as the related topics of using gamification in machine learning education and machine learning in gamification research. By performing a systematic literature mapping, we not only identify prior works addressing these respective themes, but also analyze how their popularity evolved in time, investigate the areas of application reported by prior works, used machine learning techniques and software tools, as well as the character of research contribution and the character of evaluation results for works that presented them. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence and Machine Learning in Games)
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22 pages, 7275 KiB  
Article
ChatGPT Challenges Blended Learning Methodologies in Engineering Education: A Case Study in Mathematics
by Luis M. Sánchez-Ruiz, Santiago Moll-López, Adolfo Nuñez-Pérez, José Antonio Moraño-Fernández and Erika Vega-Fleitas
Appl. Sci. 2023, 13(10), 6039; https://doi.org/10.3390/app13106039 - 14 May 2023
Cited by 22 | Viewed by 7004
Abstract
This research aims to explore the potential impact of the ChatGPT on b-learning methodologies in engineering education, specifically in mathematics. The study focuses on how the use of these artificial intelligence tools can affect the acquisition of critical thinking, problem-solving, and group work [...] Read more.
This research aims to explore the potential impact of the ChatGPT on b-learning methodologies in engineering education, specifically in mathematics. The study focuses on how the use of these artificial intelligence tools can affect the acquisition of critical thinking, problem-solving, and group work skills among students. The research also analyzes the students’ perception of the reliability, usefulness, and importance of these tools in academia. The study collected data through a survey of 110 students enrolled in a Mathematics I course in BEng Aerospace Engineering where a blended methodology, including flipped teaching, escape room gamification, problem-solving, and laboratory sessions and exams with a computer algebraic system were used. The data collected were analyzed using statistical methods and tests for significance. Results indicate students have quickly adopted ChatGPT tool, exhibiting high confidence in their responses (3.4/5) and general usage in the learning process (3.61/5), alongside a positive evaluation. However, concerns arose regarding the potential impact on developing lateral competencies essential for future engineers (2.8/5). The study concludes that the use of ChatGPT in blended learning methodologies poses new challenges for education in engineering, which requires the adaptation of teaching strategies and methodologies to ensure the development of essential skills for future engineers. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence and Machine Learning in Games)
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18 pages, 6121 KiB  
Article
Research on Wargame Decision-Making Method Based on Multi-Agent Deep Deterministic Policy Gradient
by Sheng Yu, Wei Zhu and Yong Wang
Appl. Sci. 2023, 13(7), 4569; https://doi.org/10.3390/app13074569 - 04 Apr 2023
Viewed by 2322
Abstract
Wargames are essential simulators for various war scenarios. However, the increasing pace of warfare has rendered traditional wargame decision-making methods inadequate. To address this challenge, wargame-assisted decision-making methods that leverage artificial intelligence techniques, notably reinforcement learning, have emerged as a promising solution. The [...] Read more.
Wargames are essential simulators for various war scenarios. However, the increasing pace of warfare has rendered traditional wargame decision-making methods inadequate. To address this challenge, wargame-assisted decision-making methods that leverage artificial intelligence techniques, notably reinforcement learning, have emerged as a promising solution. The current wargame environment is beset by a large decision space and sparse rewards, presenting obstacles to optimizing decision-making methods. To overcome these hurdles, a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) based wargame decision-making method is presented. The Partially Observable Markov Decision Process (POMDP), joint action-value function, and the Gumbel-Softmax estimator are applied to optimize MADDPG in order to adapt to the wargame environment. Furthermore, a wargame decision-making method based on the improved MADDPG algorithm is proposed. Using supervised learning in the proposed approach, the training efficiency is improved and the space for manipulation before the reinforcement learning phase is reduced. In addition, a policy gradient estimator is incorporated to reduce the action space and to obtain the global optimal solution. Furthermore, an additional reward function is designed to address the sparse reward problem. The experimental results demonstrate that our proposed wargame decision-making method outperforms the pre-optimization algorithm and other algorithms based on the AC framework in the wargame environment. Our approach offers a promising solution to the challenging problem of decision-making in wargame scenarios, particularly given the increasing speed and complexity of modern warfare. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence and Machine Learning in Games)
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15 pages, 2734 KiB  
Article
Admission-Based Reinforcement-Learning Algorithm in Sequential Social Dilemmas
by Ting Guo, Yuyu Yuan and Pengqian Zhao
Appl. Sci. 2023, 13(3), 1807; https://doi.org/10.3390/app13031807 - 31 Jan 2023
Cited by 2 | Viewed by 1408
Abstract
Recently, the social dilemma problem is no longer limited to unrealistic stateless matrix games but has been extended to temporally and spatially extended Markov games by multi-agent reinforcement learning. Many multi-agent reinforcement-learning algorithms have been proposed to solve sequential social dilemmas. However, most [...] Read more.
Recently, the social dilemma problem is no longer limited to unrealistic stateless matrix games but has been extended to temporally and spatially extended Markov games by multi-agent reinforcement learning. Many multi-agent reinforcement-learning algorithms have been proposed to solve sequential social dilemmas. However, most current algorithms focus on cooperation to improve the overall reward while ignoring the equality among agents, which could be improved in terms of practicality. Here, we propose a novel admission-based hierarchical multi-agent reinforcement-learning algorithm to promote cooperation and equality among agents. We extend the give-or-take-some model to Markov games, decompose the fairness of each agent, and propose an Admission reward. For better learning, we design a hierarchy consisting of a high-level policy and multiple low-level policies, where the high-level policy maximizes the Admission reward by choosing different low-level policies to interact with environments. In addition, the learning and execution of policies are realized through a decentralized method. We conduct experiments in multiple sequential social dilemmas environments and show that the Admission algorithm significantly outperforms the baselines, demonstrating that our algorithm can learn cooperation and equality well. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence and Machine Learning in Games)
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17 pages, 1303 KiB  
Article
Performance Analysis of Reinforcement Learning Techniques for Augmented Experience Training Using Generative Adversarial Networks
by Smita Mahajan, Shruti Patil, Moinuddin Bhavnagri, Rashmi Singh, Kshitiz Kalra, Bhumika Saini, Ketan Kotecha and Jatinderkumar Saini
Appl. Sci. 2022, 12(24), 12923; https://doi.org/10.3390/app122412923 - 16 Dec 2022
Viewed by 1703
Abstract
This paper aims at analyzing the performance of reinforcement learning (RL) agents when trained in environments created by a generative adversarial network (GAN). This is a first step towards the greater goal of developing fast-learning and robust RL agents by leveraging the power [...] Read more.
This paper aims at analyzing the performance of reinforcement learning (RL) agents when trained in environments created by a generative adversarial network (GAN). This is a first step towards the greater goal of developing fast-learning and robust RL agents by leveraging the power of GANs for environment generation. The RL techniques that we tested were exact Q-learning, approximate Q-learning, approximate SARSA and a heuristic agent. The task for the agents was to learn how to play the game Super Mario Bros (SMB). This analysis will be helpful in suggesting which RL techniques are best suited for augmented experience training (with synthetic environments). This would further help in establishing a reinforcement learning framework using the agents that can learn faster by bringing a greater variety in environment exploration. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence and Machine Learning in Games)
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