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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 September 2025 | Viewed by 2938

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 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. 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

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Related Special Issue

Published Papers (3 papers)

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Research

11 pages, 1662 KiB  
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
Viewed by 158
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 KiB  
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 1144
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 KiB  
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
Viewed by 1122
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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