Game AI Research Articles

Using a Novel Gameplay Intervention to Target Intrusive Memories After Work-Related Trauma: Iterative Qualitative Analysis of Intensive Care Unit Staff Experiences.

Many intensive care unit (ICU) staff experience intrusive memories following work-related traumatic events, which can lead to long-term mental health outcomes and impact work functioning. There is a need for interventions that target intrusive memories in this population; however, factors such as mental health stigma and difficulty in fitting interventions into busy schedules can pose barriers. The Brief Gameplay Intervention For National Health Service Intensive Care Unit Staff Affected By COVID-19 Trauma (GAINS) study tested a brief, digital imagery-competing task intervention (including computer gameplay) with the aim of reducing the recurrence of intrusive memories, which holds promise for overcoming some of these barriers. This substudy aims to explore barriers and facilitators to the uptake and practical use of the intervention by ICU staff, along with its acceptability, and iteratively explore the impact of intervention optimizations to further refine the intervention. The GAINS study is a randomized controlled trial comparing access to a brief digital imagery-competing task intervention for 4 weeks with usual care followed by delayed access to the intervention. The participants were ICU staff who worked during the COVID-19 pandemic and experienced intrusive memories. All participants were sent a questionnaire at 4 weeks to gather data about intervention acceptability. Nested within the randomized controlled trial, a subset of 16 participants was interviewed, and data were analyzed using thematic analysis drawing from a framework approach. Both quantitative and qualitative data indicated high acceptability of the intervention. Intervention use data show that, on average, staff were able to target approximately 73% (3.64/4.88) of their intrusive memories and engaged with the Tetris component for the full 20 minutes per session. Overall, on the acceptability questionnaire, staff found the intervention easy to use, helpful, and highly acceptable. The interviews generated four themes: approach to the intervention, positives of the intervention, negatives of the intervention, and improvements and optimizations. Findings highlighted barriers that ICU staff experienced: stigma, feeling weak for seeking help, not wanting colleagues to know they were struggling, and skepticism. However, they provided suggestions on how barriers could be overcome and discussed the advantages of the intervention when compared with other treatments. Although participants described many positive aspects of the intervention, such as being easy to use, enjoyable, and leading to a reduction in the frequency or intensity of intrusive memories, they also raised practical issues for implementation. The intervention has the potential to overcome stigma and reduce the frequency of intrusive memories after traumatic events among ICU staff. Further refinement is needed to improve the adoption and reach of this intervention. A limitation is that we could not interview the National Health Service staff who were unable or unwilling to take part in the trial.

Dopamine and serotonin in human substantia nigra track social context and value signals during economic exchange.

Dopamine and serotonin are hypothesized to guide social behaviours. In humans, however, we have not yet been able to study neuromodulator dynamics as social interaction unfolds. Here, we obtained subsecond estimates of dopamine and serotonin from human substantia nigra pars reticulata during the ultimatum game. Participants, who were patients with Parkinson's disease undergoing awake brain surgery, had to accept or reject monetary offers of varying fairness from human and computer players. They rejected more offers in the human than the computer condition, an effect of social context associated with higher overall levels of dopamine but not serotonin. Regardless of the social context, relative changes in dopamine tracked trial-by-trial changes in offer value-akin to reward prediction errors-whereas serotonin tracked the current offer value. These results show that dopamine and serotonin fluctuations in one of the basal ganglia's main output structures reflect distinct social context and value signals.

The application of artificial intelligence in the game field

With the rapid development of artificial intelligence technology, its application in the field of games is increasingly widespread. At present, the application of artificial intelligence in games has become a research hotspot. This article mainly explores the application of AI in various types of games such as first person shooter (FPS), Role playing Games (RPGs), Strategy, and analyzes their advantages and disadvantages based on the category of the game. Specifically, this article explores the application of AI in FPS games, focusing on how AI controls the behavior and strategies of enemies to provide players with challenging and realistic experiences. In the context of RPGs, the paper analyzes AI's impact on intelligent decision-making and interaction with non-playable characters (NPCs). Intelligent NPCs create a more authentic and immersive interaction between players and game characters. In the case of strategy games, AI enhances the strategic and challenging aspects, offering a more layered and dynamic gaming experience, and pushing players' decision-making abilities and strategic thinking. Additionally, the article discusses the limitations and challenges of AI in games. This paper summarizes and prospects the application of AI in the field of games.

Analysis of the influence of artificial intelligence technology on the immersion of game players

Since the application of artificial intelligence in games, a variety of different games have come out, and more and more people consume their rest time by playing games as entertainment. This paper mainly studies the influence of artificial intelligence technology on the immersion of game players and its reflection on the development of games. The purpose of the research is to prove how artificial intelligence technology has an impact on the immersion of game players and what aspects of the game. Mainly through the analysis of the relationship between artificial intelligence and game development, peoples views on the application of artificial intelligence in games and feedback to study. This paper finds that artificial intelligence technology has a very positive and positive impact on the immersion of game players and still has a profound impact on the future development of games.

Hygienic assessment of gaming conditions in the context of studying health risk factors for video game players

The article investigates the impact of regular engagement in computer games on the health of young individuals. Survey results indicate that players encounter issues with the musculoskeletal system and visual analyzer, including muscle pain, headaches, numbness, and spasms. An analysis of psychophysiological and social aspects reveals that esports athletes face not only physical problems but also psychological stress. Identified psychological issues include mood decline, anxiety, and irritability. The authors identify key health risk factors for esports athletes, such as high gaming frequency and duration, signs of computer dependency, lack of specialized equipment and furniture, consumption of stimulating substances during gaming, sedentary lifestyle, and sleep disturbances. The research underscores the importance of preventive measures and raising awareness of health risks among computer game players. The findings emphasize the need for a comprehensive approach to the well-being of this demographic, addressing both physical and mental aspects. The study contributes valuable insights into the multifaceted challenges associated with regular computer gaming, providing a foundation for developing strategies to mitigate potential health risks. Implementing preventive measures and fostering an understanding of the risks involved are crucial for promoting the health and well-being of individuals engaged in computer gaming activities.

Analysis of general physical performance of students-eSportsmen

Nowadays, eSports – competitive activities in computer games – are an integral part of sports culture. There are a number of studies confirming the similarities between traditional sports and eSports, athletes and eSportsmen, computer game play and physical exercise. In order to improve their performance, eSportsmen use the same methods as traditional athletes: following a daily regime, a clear training schedule, having a coach and a psychologist. At the same time, there is a perception that eSports has a negative impact on players’ physical fitness: hypodynamia, hours of daily gaming sessions and monotonous low-amplitude movements are not associated with a healthy lifestyle. This study analyzed the physical performance of ten men (aged between 19 and 21) who had been involved in eSports for 5 years. The method used was the Harvard-step test, which requires men to climb a 50 cm high bench at a given speed for 5 minutes. Before and after performing the test, biological indicators such as heart rate, blood pressure and a number of other indicators are recorded for the first 30 seconds, during the second, third and fourth minutes. The results showed that most of the subjects in this group performed above average on the step test, indicating that the physical performance of young eSportsmen is at a relatively high level, which contradicts the common perception of their poor physical fitness.

Метод оптимизации организационной структуры централизованных многоагентных систем в автоматическом режиме

The organizational structure of a multi-agent system (MAS) is a set of roles and relationships of agents, com-ponents that control their behavior, as well as rules governing the interaction of system elements. The effectiveness of the MAS largely depends on the characteristics of the organizational structure used. Existing solutions in this area have a significant disadvantage, namely low adaptability to changes in environmental parameters or adjustment of the conditions of the task, which consists in the need to restart the procedure for synthesizing the organizational structure of the MAS. The problem of automatic optimization of the organizational structure of centralized MAS in the conditions of changed environmental parameters or the task is of particular relevance for centralized MAS with a strict hierarchical structure, whose agents can be divided into two classes – agents-managers, agents-subordinates. The subject of the study is the methods of synthesis of the organizational structure of the MAS. The aim of the work is to develop a method for optimizing the organizational structure of centralized MAS in automatic mode. To achieve this goal, the concepts of primary and secondary agent resources were introduced, based on a biogeographic algorithm, a method was developed that regulates the movement of subordinate agents between groups depending on the degree of attractiveness of the agent manager (depends on the sum of estimates of the effectiveness of subordinate agents managed by him, as well as on the distance from this subordinate agent). The developed method can find its practical application in the implementation of the following tasks: inspection or patrolling (protection) of infrastructure facilities by mobile robots, the implementation of artificial intelligence in computer games.

A research of artificial intelligence game agent application

Currently, large language models are on the rise with breakthrough progress in artificial intelligence. Existing reviews of AI game agents have not covered these latest developments, requiring a combing and analysis of the newest research advancements in game AI agents. This paper summarizes the application scenarios of game AI agents in four aspects: combat AI, Non-Player Character (NPC) interaction, automated testing, and Artificial General Intelligence (AGI) testing. In combat AI, there is a progressive developmental trend, with the introduction of Monte Carlo tree search and reinforcement learning enabling AI game agents to fully surpass humans in traditional board games. In NPC interaction, full AI is unnecessary. Game developers only need to incorporate AI for abilities related to player experience to increase appeal, with controllable generation results. In automated testing, game AI agents lack generalizability for testing so far. In AGI testing, academia has helpfully explored general game AI, but capabilities remain limited to certain games. Introducing large language models to game AI agents shows unprecedented capabilities. Finally, this paper provides an outlook on the hot topics and future directions of this research subject.

Physical Activity and its Correlates among University Students in Lahore

Background: Physical activity plays a crucial role in maintaining overall health, yet global trends indicate a decline in these levels among university students. Gender differences, environmental access, and lifestyle choices have been identified as significant factors influencing physical activity levels. Previous studies have offered insights into these dynamics, yet gaps remain in understanding the comprehensive impact of various determinants on physical activity among the youth. Objective: This study aimed to assess the levels of physical activity and their association with different factors among university students, with a focus on identifying gender disparities and the influence of environmental and lifestyle factors on physical activity levels. Methods: A cross-sectional study was conducted among students aged 18 to 30 years at Akhtar Saeed Medical and Dental College (AMDC) and the University of Lahore (UOL) from July 2022 to January 2023. Ethical approval was obtained, and a sample size of 150 students was determined using EPI software. Data on physical activity levels were collected using the International Physical Activity Questionnaire (IPA-Q) and analyzed using SPSS version 25. Chi-square tests were applied to assess the association between physical activity levels and factors such as computer game play, music listening, sports participation during school, parental encouragement, and institutional support. Results: Among the participants, a significant gender difference in physical activity levels was observed, with males engaging more in strenuous activities. The study found a high association of physical activity with being an active sportsman during school and college (Chi-Square = 7.194, P-value = 0.02) and listening to music (Chi-Square = 9.27, P-value = 0.01). A majority of the students fell into the mild physical activity category, with fewer participants involved in moderate and high physical activity levels. No significant association was found with computer game playing, parental encouragement, or institutional support for physical activity. Conclusion: The study highlights significant gender differences in physical activity levels among university students, with males being more active. Early sports involvement and music listening emerged as key factors associated with higher physical activity levels. The findings suggest the need for targeted interventions to enhance physical activity, particularly among female students, and promote a more active lifestyle within the university setting.

The application of artificial intelligence in FPS Games

It has been a long time since games and artificial intelligence(AI) influenced each other. However, when it comes to the area of the application of AI in games most people talk about chess games. AI also plays an important role in First-person shooter games. First-person shooter games, also known as FPS games, provide abundant testing environments for developing and testing artificial intelligence algorithms too, and the advances in artificial intelligence have allowed the game industry to produce better FPS games. This essay is a iterature review that summarizes why and how people apply AI in FPS games, in both players aspect and the game industry aspect. This essay briefly introduce the current research status, analyzes current challenges, and predicts future research directions by studying the application of artificial intelligence in FPS games as well as introduce the application of artificial intelligence in FPS games, explore the application of artificial intelligence in the gaming industry and its impact on FPS game development, and provide reference information for relevant research work.

Comparison of different algorithms in Reversi AI

Minimax and alpha-beta pruning have been widely applied in AI for various strategic board games, the utilization of greedy algorithms in this context has received less attention. The Greedy algorithms aim to make locally optimal choices at each step, exploiting immediate gains. This research aims to reveal the potential benefits and limitations of applying greedy algorithms in Reversi gaming AI, specifically through a comparison with the Minimax algorithm. A series of AI versus AI matches were conducted to evaluate and compare the performance of the three different AI algorithms. The objective was to assess their gameplay strategies and decision-making abilities by measuring their average execution time and win rates. Relevant codes and experiments will be carried out in a C++ environment, and the shown codes in this article will only have pseudocode and comments. In conclusion, the findings of this study indicate that the Greedy Algorithm is not a superior alternative to the Minimax Algorithm in competitive scenarios, particularly with increased searching depth. However, greedy algorithms still have weak competitiveness with reduced computational time. For future research, concentrating on improving the performance of the Greedy Algorithm by incorporating more advanced heuristics or adaptive strategies maybe a good choice. Additionally, combining the strengths of both the Greedy Algorithm and the Minimax Algorithm could be a promising direction for further investigation.

A Comparative Study on Factors Affecting the User’s Satisfaction of Gaming Platforms: A Mobile Device or Desktop Computer

The research purpose : (1) The factors affecting the satisfaction of mobile devices and desktop players of MMORPG games in China are discussed. (2) Investigate the relative importance of the satisfaction of MMORPG players on mobile devices and desktop computer in China. (3) According to the research results, make a guide for MMORPG players in the choice of platform, and give relative suggestions, so that players can choose their own game platform more quickly. Using this quantitative research, mainly MMORPG players in Hunan Province, including players from other provinces. By publishing questionnaire links in related forums, social media platforms and related official topics, 1,258 valid questionnaires were collected after cleaning, and the methods of reliability analysis, validity analysis, variance analysis and regression analysis were used in the study. It is found that: (1) Game quality, social rating, narrative structure and game experience will have a positive impact on player satisfaction. (2) China MMORPG desktop computer players are more satisfied than mobile devices players. (3) Suggestions are made that future research should further explore the differences between specific game types and specific player groups, so as to better meet the needs of different users and better guide gamers.

The Role of AI in Serious Games and Gamification for Health: Scoping Review.

Artificial intelligence (AI) and game-based methods such as serious games or gamification are both emerging technologies and methodologies in health care. The merging of the two could provide greater advantages, particularly in the field of therapeutic interventions in medicine. This scoping review sought to generate an overview of the currently existing literature on the connection of AI and game-based approaches in health care. The primary objectives were to cluster studies by disease and health topic addressed, level of care, and AI or games technology. For this scoping review, the databases PubMed, Scopus, IEEE Xplore, Cochrane Library, and PubPsych were comprehensively searched on February 2, 2022. Two independent authors conducted the screening process using Rayyan software (Rayyan Systems Inc). Only original studies published in English since 1992 were eligible for inclusion. The studies had to involve aspects of therapy or education in medicine and the use of AI in combination with game-based approaches. Each publication was coded for basic characteristics, including the population, intervention, comparison, and outcomes (PICO) criteria; the level of evidence; the disease and health issue; the level of care; the game variant; the AI technology; and the function type. Inductive coding was used to identify the patterns, themes, and categories in the data. Individual codings were analyzed and summarized narratively. A total of 16 papers met all inclusion criteria. Most of the studies (10/16, 63%) were conducted in disease rehabilitation, tackling motion impairment (eg, after stroke or trauma). Another cluster of studies (3/16, 19%) was found in the detection and rehabilitation of cognitive impairment. Machine learning was the main AI technology applied and serious games the main game-based approach used. However, direct interaction between the technologies occurred only in 3 (19%) of the 16 studies. The included studies all show very limited quality evidence. From the patients' and healthy individuals' perspective, generally high usability, motivation, and satisfaction were found. The review shows limited quality of evidence for the combination of AI and games in health care. Most of the included studies were nonrandomized pilot studies with few participants (14/16, 88%). This leads to a high risk for a range of biases and limits overall conclusions. However, the first results present a broad scope of possible applications, especially in motion and cognitive impairment, as well as positive perceptions by patients. In future, the development of adaptive game designs with direct interaction between AI and games seems promising and should be a topic for future reviews.

Self-Governing Hybrid Societies and Deception

Self-governing hybrid societies are multi-agent systems where humans and machines interact by adapting to each other’s behaviour. Advancements in Artificial Intelligence (AI) have brought an increasing hybridisation of our societies, where one particular type of behaviour has become more and more prevalent, namely deception. Deceptive behaviour as the propagation of disinformation can have negative effects on a society’s ability to govern itself. However, self-governing societies have the ability to respond to various phenomena. In this paper we explore how they respond to the phenomenon of deception from an evolutionary perspective considering that agents have limited adaptation skills. Will hybrid societies fail to govern deceptive behaviour and reach a Tragedy of The Digital Commons? Or will they manage to avoid it through cooperation? How resilient are they against large-scale deceptive attacks? We provide a tentative answer to some of these questions through the lens of evolutionary agent-based modelling, based on the scientific literature on deceptive AI and public goods games.

Implementation of artificial intelligence in "ToqyzQumalaq" mobile logic game

This research paper delves into the implementation of artificial intelligence (AI) in the mobile logic game "ToqyzQumalaq," focusing on incorporating advanced algorithmic strategies to improve gameplay. The game's complexity and strategic depth present unique challenges in AI development, addressed through the integration of algorithms like Minimax, Alpha-Beta Pruning, Greedy, and Particle Swarm Optimization (PSO). The study emphasizes the creation of evaluation functions for these algorithms, ensuring AI efficiency and human-like decision-making. This aspect is vital for maintaining the strategic unpredictability essential to "ToqyzQumalaq." Extensive experimental testing against human players of various skill levels demonstrates the algorithms' effectiveness. These tests reveal the strengths and limitations of each algorithm, providing insights into their application in the game. This paper contributes to AI in gaming, highlighting the challenges and opportunities in developing AI for complex games. Its findings are relevant not only to game developers but also serve as an educational tool, showcasing the practical application of AI and algorithmic strategies.

Promptable Game Models: Text-guided Game Simulation via Masked Diffusion Models

Neural video game simulators emerged as powerful tools to generate and edit videos. Their idea is to represent games as the evolution of an environment’s state driven by the actions of its agents. While such a paradigm enables users to play a game action-by-action, its rigidity precludes more semantic forms of control. To overcome this limitation, we augment game models with prompts specified as a set of natural language actions and desired states . The result—a Promptable Game Model (PGM)—makes it possible for a user to play the game by prompting it with high- and low-level action sequences. Most captivatingly, our PGM unlocks the director’s mode , where the game is played by specifying goals for the agents in the form of a prompt. This requires learning “game AI,” encapsulated by our animation model, to navigate the scene using high-level constraints, play against an adversary, and devise a strategy to win a point. To render the resulting state, we use a compositional NeRF representation encapsulated in our synthesis model. To foster future research, we present newly collected, annotated and calibrated Tennis and Minecraft datasets. Our method significantly outperforms existing neural video game simulators in terms of rendering quality and unlocks applications beyond the capabilities of the current state-of-the-art. Our framework, data, and models are available at snap-research.github.io/promptable-game-models.

The Ethics of AI in Games

Video games are one of the richest and most popular forms of human-computer interaction and, hence, their role is critical for our understanding of human behaviour and affect at a large scale. As artificial intelligence (AI) tools are gradually adopted by the game industry a series of ethical concerns arise. Such concerns, however, have so far not been extensively discussed in a video game context. Motivated by the lack of a comprehensive review on the ethics of AI as applied to games, we survey the current state of the art in this area and discuss ethical considerations of these systems from the holistic perspective of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">affective loop</i> . Through the components of this loop, we study the ethical challenges that AI faces in video game development. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Elicitation</i> highlights the ethical boundaries of artificially induced emotions; <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sensing</i> showcases the trade-off between privacy and safe gaming spaces; and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">detection</i> , as utilised during in-game <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adaptation</i> , poses challenges to transparency and ownership. This paper calls for an open dialogue and action for the games of today and the virtual spaces of the future. By setting an appropriate framework we aim to protect users and to guide developers towards safer and better experiences for their customers.

Development of Two Dimension (2D) Game Engine with Finite State Machine (FSM) Based Artificial Intelligence (AI) Subsystem

With Al becoming more and more relevant in today’s world, this project aims to develop a 2D game engine with an Al subsystem for state-driven agents, which is rarely implemented by a lot of 2D engines out there. In this study, a 2D game engine was designed with an FSM (Finite State Machine)--based AL subsystem using state-driven game agents. The engine was implemented using the Javascript programming language and the WebGL 2.0 graphics library/API. It is targeted at web-based games/simulations. Components and subsystems include physics, audio, math, rendering, and AI (based on finite-state machines). The FSM-based AI subsystem is a solution aimed at reducing the ambiguity and performance hits associated with creating 2D game AI in the naive approach. The AI subsystem creates an interface for 2D games to be created with a common paradigm, and simulated with a great level of realism. The state machine used in this study is used to represent a variety of behaviours, such as wandering, attacking, and fleeing. The following conclusions were drawn as regards the impact of the AI approach used on rendering performance; the naive approach to implementing game AI is also compared with the FSM approach in terms of rendering (frames per second/ FPS, or frame rate). With the naive approach (vector math) being used to implement AI, there was a drop in the rendering frame rate to 50 FPS. The FSM approach didn’t affect the frame rate, which is usually at 60FPS. With a well-developed FSM (Finite State Machine), game agents can transition between states easily as a response to user input or stimulus from the game environment. The proposed method was tested by creating a prototype game with the 2D game engine. The prototype game was a straightforward side-scrolling platformer featuring a cast of non-player characters (NPCs). This approach to implementing 2D game AI goes a long way toward improving performance and mitigating ambiguity in the code.

Forecasting Future Behavior: Agents in Board Game Strategy

This paper presents findings on machine learning agent behavior prediction in a board game application developed by a group of students. The goal of this research is to create a model facilitating collaboration between a user and an AI to play together in the board game using a Human-in-the-Loop architecture. By injecting explainability, the aim is to enhance communication and understanding between the user and the AI agent. Featuring a competitive Artificial Intelligence (AI) based on the Proximal Policy Optimization model, this research explores methods to make AI decisions transparent for enhanced player understanding. Two predictive models, a Decision Tree (DT) and a Deep Learning (DL) classifier, were developed and compared. The results show that the DT model is effective for short-term predictions but limited in broader applications, while the DL classifier shows potential for long-term prediction without requiring direct access to the game's AI. This study contributes to understanding human-AI interaction in gaming and offers insights into AI decision-making processes.

Model Predictive Control and Reinforcement Learning: A Unified Framework Based on Dynamic Programming

In this paper we describe a new conceptual framework that connects approximate Dynamic Programming (DP), Model Predictive Control (MPC), and Reinforcement Learning (RL). This framework centers around two algorithms, which are designed largely independently of each other and operate in synergy through the powerful mechanism of Newton's method. We call them the off-line training and the on-line play algorithms. The names are borrowed from some of the major successes of RL involving games; primary examples are the recent (2017) AlphaZero program (which plays chess, [SHS17], [SSS17]), and the similarly structured and earlier (1990s) TD-Gammon program (which plays backgammon, [Tes94], [Tes95], [TeG96]). In these game contexts, the off-line training algorithm is the method used to teach the program how to evaluate positions and to generate good moves at any given position, while the on-line play algorithm is the method used to play in real time against human or computer opponents. Significantly, the synergy between off-line training and on-line play also underlies MPC (as well as other major classes of sequential decision problems), and indeed the MPC design architecture is very similar to the one of AlphaZero and TD-Gammon. This conceptual insight provides a vehicle for bridging the cultural gap between RL and MPC, and sheds new light on some fundamental issues in MPC. These include the enhancement of stability properties through rollout, the treatment of uncertainty through the use of certainty equivalence, the resilience of MPC in adaptive control settings that involve changing system parameters, and the insights provided by the superlinear performance bounds implied by Newton's method.