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Applications of Artificial Intelligence and Machine Learning in Games: 2nd Edition

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 May 2026 | Viewed by 23834

Special Issue Editors

Dr. Mozgovoy Maxim

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 Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Since the early years of computing, games have been used as testing environments for new artificial intelligence (AI) methods and technologies. The study of game worlds, ranging from checkers and chess to Go and StarCraft, have greatly contributed to previous achievements in 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 will provide a venue for discussing all current topics of game-based AI research. We invite works reporting original research results, as well as review and opinion papers.

Dr. Mozgovoy Maxim
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 250 words) can be sent to the Editorial Office for assessment.

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

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Related Special Issue

Published Papers (7 papers)

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Research

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10 pages, 2063 KB  
Article
Dynamic Difficulty Adjustment with Machine Learning for Air Hockey
by Mikhail Zgonnikov and Maxim Mozgovoy
Appl. Sci. 2026, 16(6), 2947; https://doi.org/10.3390/app16062947 - 18 Mar 2026
Viewed by 503
Abstract
This work presents a method for implementing dynamic difficulty adjustment in the arcade game of Air Hockey using reinforcement learning. The resulting AI-controlled opponent is capable of adapting its skill level to the player’s performance to maintain engagement and provide a balanced gameplay [...] Read more.
This work presents a method for implementing dynamic difficulty adjustment in the arcade game of Air Hockey using reinforcement learning. The resulting AI-controlled opponent is capable of adapting its skill level to the player’s performance to maintain engagement and provide a balanced gameplay experience. The approach relies on generating several AI agents through progressively longer training durations, resulting in distinct and smoothly transitioning difficulty levels that can be switched dynamically. We discuss how this scheme can be extended with manually selected parameters that influence physical aspects of the agent’s behavior—such as movement speed, reaction latency, and control precision—to complement the variations in decision-making quality. The proposed method is applicable to a wide range of video games, and experimental results demonstrate its effectiveness in producing adaptive and varied opponent behavior. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence and Machine Learning in Games: 2nd Edition)
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23 pages, 431 KB  
Article
Supporting Serious Game Development with Generative Artificial Intelligence: Mapping Solutions to Lifecycle Stages
by Jakub Swacha and Michał Gracel
Appl. Sci. 2025, 15(21), 11606; https://doi.org/10.3390/app152111606 - 30 Oct 2025
Cited by 5 | Viewed by 5485
Abstract
The emergence of effective Generative AI has sparked a revolution in video game development, enabling us to generate game assets and source code at a fraction of the cost and time needed compared to if human developers were involved. But the support available [...] Read more.
The emergence of effective Generative AI has sparked a revolution in video game development, enabling us to generate game assets and source code at a fraction of the cost and time needed compared to if human developers were involved. But the support available from GenAI goes far beyond the generation of game assets and code, especially in the case of serious games, which have to combine playability with non-entertainment purposes such as, but not only, education. In this paper, the potential forms of GenAI-based support for serious game development are explored and placed into the context of the respective phases of the serious game development lifecycle. As existing lifecycle models are either not specialized for the specifics of serious games or are otherwise too simple, a new serious game development lifecycle model has been proposed for this purpose. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence and Machine Learning in Games: 2nd Edition)
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26 pages, 17581 KB  
Article
The Novice, the Expert, and the Algorithm: A Comparative Analysis of Human Expertise Transfer and AI Performance in Audio-Only Gaming Environments
by Ibrahim Khan, Thai Van Nguyen, Cvetković Tijan Juraj and Ruck Thawonmas
Appl. Sci. 2025, 15(21), 11594; https://doi.org/10.3390/app152111594 - 30 Oct 2025
Viewed by 1330
Abstract
This study provides a symmetrical, cross-genre comparison of human expertise transfer and “blind” artificial intelligence (AI) performance in audio-only gaming environments. Although previous research has focused on human performance in audio games and the feasibility of blind agents trained on auditory inputs separately, [...] Read more.
This study provides a symmetrical, cross-genre comparison of human expertise transfer and “blind” artificial intelligence (AI) performance in audio-only gaming environments. Although previous research has focused on human performance in audio games and the feasibility of blind agents trained on auditory inputs separately, a direct comparison of these two forms of expertise is missing. We fill this gap with a robust experimental design, involving 37 human players (aged 18–44), grouped by gaming experience and specialized blind AI agents. We measured key performance variables, including win ratios, health differences, and task completion times across two genres: a fighting game (DareFightingICE) and a first-person shooter (SonicDoom). Our findings show a complex, task-dependent relationship. In DareFightingICE, expert humans (73.0% win ratio) significantly outperformed the AI (54.0% win ratio), demonstrating effective cognitive transfer. Meanwhile, the AI’s performance matched the overall human average (54.0% vs. 53.0%). Conversely, in SonicDoom, AI achieved superhuman speed in simple tasks (1.55 s vs. 5.35 s) but underperformed compared to expert humans in complex scenarios, highlighting that the AI’s proficiency is specialized but fragile, whereas human expertise is more robust and adaptable. The results provide practical insights for audio-rich game design and highlight the crucial need for AI models beyond reactive policies. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence and Machine Learning in Games: 2nd Edition)
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11 pages, 1662 KB  
Article
Engagement-Oriented Dynamic Difficulty Adjustment
by Qingwei Mi and Tianhan Gao
Appl. Sci. 2025, 15(10), 5610; https://doi.org/10.3390/app15105610 - 17 May 2025
Cited by 4 | Viewed by 4345
Abstract
As an emerging and lively research field, game designers are employing Dynamic Difficulty Adjustment (DDA) in Game Artificial Intelligence (Game AI) to improve player experience. Traditional DDA methods focus little on player churn, which cannot always lead to enhanced player engagement. Hence, we [...] Read more.
As an emerging and lively research field, game designers are employing Dynamic Difficulty Adjustment (DDA) in Game Artificial Intelligence (Game AI) to improve player experience. Traditional DDA methods focus little on player churn, which cannot always lead to enhanced player engagement. Hence, we propose the Engagement-oriented Dynamic Difficulty Adjustment (EDDA) to meet the urgent need for a highly general and customizable solution in the game industry. EDDA directly considers players’ churn trend to ensure player engagement during gameplay. Its real-time monitoring algorithm and common parameter set are effective in quantifying and preventing player churn. We developed a prototype system integrating seven major game genres to verify the difficulty, gameplay time, and scores of the Game Engagement Questionnaire (GEQ) in multiple dimensions. EDDA has the largest mean and median of all genres in the above metrics with the highest confidence level and effect size, which demonstrates its generality and availability in improving player experience. It is fair to say that EDDA not only provides game designers with targeted player churn monitoring and intervention means, but also offers a deeper level of thinking for the generalized application of DDA and other Game AI technologies. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence and Machine Learning in Games: 2nd Edition)
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15 pages, 820 KB  
Article
Design and Implementation of a Compiled Declarative Language for Game AI Control
by Christopher Cromer, Martin Araneda and Clemente Rubio-Manzano
Appl. Sci. 2025, 15(1), 157; https://doi.org/10.3390/app15010157 - 27 Dec 2024
Viewed by 2524
Abstract
Video games have become one of the most popular forms of entertainment around the world. Currently, agents (bots or non-player characters) are predominantly programmed using procedural and deterministic imperative techniques, which pose significant drawbacks in terms of cost and time efficiency. An interesting [...] Read more.
Video games have become one of the most popular forms of entertainment around the world. Currently, agents (bots or non-player characters) are predominantly programmed using procedural and deterministic imperative techniques, which pose significant drawbacks in terms of cost and time efficiency. An interesting and alternative line of work is to develop declarative scripting languages which align the programming task closer to human logic. This allows programmers to intuitively implement agents’ behaviors using straightforward rules. In this regard, most of these languages are interpreted, which may impact performance. Hence, this article presents the design and implementation of a new declarative and compiled scripting language called Obelysk for controlling agents. To test and evaluate the language, a video game was created using the Godot game engine, which allowed us to demonstrate the correct functionality of our scripting language to program the AIs participating in the video game. Finally, an analytics platform was also developed to evaluate the correct behavior of the programmed agents. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence and Machine Learning in Games: 2nd Edition)
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14 pages, 2185 KB  
Article
Analysis, Generation, and Validation of New Boards for the Game Micro Robots
by Kevin Silva, Felipe Besoain and Nicolas A. Barriga
Appl. Sci. 2024, 14(20), 9416; https://doi.org/10.3390/app14209416 - 15 Oct 2024
Cited by 1 | Viewed by 2965
Abstract
This document presents a study of the board game Micro Robots and the development of a pipeline for the generation and validation of new boards of variable sizes. The implementation was carried out in the C# language, using graph theory and a depth-first [...] Read more.
This document presents a study of the board game Micro Robots and the development of a pipeline for the generation and validation of new boards of variable sizes. The implementation was carried out in the C# language, using graph theory and a depth-first search algorithm to explore various board configurations and their possible solutions. The main objective is the creation of boards that are different from the original ones, offering greater variety in the gaming experience. The impact of the modifications on the game’s dynamics and complexity was evaluated by comparing the generated boards with those from the commercial game. The results show that no major differences were found between the new boards’ structure and the original ones, maintaining an average complexity across all configurations. This work not only contributes to the study of board game design, but also opens new opportunities for innovation in this field. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence and Machine Learning in Games: 2nd Edition)
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Review

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20 pages, 2632 KB  
Review
The Role of Artificial Intelligence in Gaming
by Antonio del Bosque, Pablo Fernández-Arias, Georgios Lampropoulos and Diego Vergara
Appl. Sci. 2025, 15(23), 12358; https://doi.org/10.3390/app152312358 - 21 Nov 2025
Viewed by 5358
Abstract
Artificial Intelligence (AI) has become a transformative force in the gaming industry, enhancing gameplay mechanics while expanding applications in education, healthcare, and human–computer interaction. The rapid growth of research in this domain requires a bibliometric review of its intellectual structure and thematic evolution. [...] Read more.
Artificial Intelligence (AI) has become a transformative force in the gaming industry, enhancing gameplay mechanics while expanding applications in education, healthcare, and human–computer interaction. The rapid growth of research in this domain requires a bibliometric review of its intellectual structure and thematic evolution. This study conducts a comprehensive bibliometric analysis of 5114 peer-reviewed documents indexed in Scopus and Web of Science between 2016 and 2025. Using performance indicators, co-authorship networks, and keyword co-occurrence mapping, the analysis identifies the most productive journals, countries, institutions, and authors contributing to the field. Citation analysis highlights seminal works on deep reinforcement learning as intellectual milestones, while keyword and thematic mapping reveal a dual trajectory: the development of advanced AI frameworks (reinforcement learning, deep learning, federated learning) and their application in societal domains such as education, psychology, and healthcare. The findings provide a consolidated overview of scientific production, intellectual influence, and thematic directions, contributing to a better understanding of the current state and prospects of AI–gaming research. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence and Machine Learning in Games: 2nd Edition)
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