Artificial intelligence in racing games

CHI Systems, under contract to the U. S. Army Research Institute, developed an immersive training system, called Virtual Environment Composable Training for Operational Readiness Training Delivery (VECTOR-TD), which provides scenario-based virtual environments for cultural familiarization. VECTOR-TD was designed to provide a new technology for game-based training in cultural familiarization through the application of scenario-based training. VECTOR-TD integrates the Lithtech Jupiter game engine for the virtual environment and CHI System’s iGEN® cognitive agent development toolkit to implement intelligent game characters and integrated performance monitoring. In order for VECTOR-TD to have true long term value to the Army, it was determined that it would be necessary to develop content management tools which would allow Army training personnel to edit and author VECTOR-TD scenarios. To address this, VECTOR-Scenario Editor (VECTOR-SE) was developed based on VECTOR-TD XML-based scripting language to externalize the representation of the training scenario, the behavior of non-player characters (NPCs) within the scenario, and the behavior of the instructor NPC responsible for performance evaluation and After Action Review (AAR). The XML-based language provides direct control of the behavior, dialog, emotional state, and predispositions of NPCs within the scenario. VECTOR-SE was successfully developed to provide content development and management tools that allow non-programmers to author scenarios and manipulate training parameters. VECTOR-SE is based on an instructional design process model and involves the specification of learning objectives, the creation of scenario segments (i.e., vignettes), the authoring of character/trainee dialog, the designation of dialog branching, the placement of characters at game locations, and the designation of when training assistance/remediation will be delivered. The significance of VECTOR-SE is twofold. First, it drastically reduces the time and skill required to develop VECTOR scenarios. Within the original VECTOR project, a 15-minute cultural training scenario was developed which required 2 months of programming and cognitive modeling effort. The same scenario was later implemented in VECTOR-SE in under 3 days. Additional scenarios with comparable complexity have also been implemented using VECTOR-SE and similar significant reductions in development time have been observed. Second, VECTOR-SE makes scenario development or modification accessible to a wider audience of professionals. VECTOR-TD and SE are currently being evaluated at the U.S. Military Academy at West Point.

Procedural content generation is helping game developers to create significant quantity of high quality dynamic content for video games at a fraction of cost of the traditional methods. Procedural texture synthesis is a sub category of procedural content generation which helps video games to have significant variations in textures of the environments and the objects across the progress of the game and to avoid repetition. Generative Adversarial Networks are a class of deep learning algorithms which are capable of learning the patterns and creating new patterns. In this paper, Generative Adversarial Networks is used for procedural content generation for original textures synthesis for video game development. This method is used by video game designers for autonomous redesigning of objects and environment textures. This process saves significant time and cost in video game development. The particular attention in this paper is on procedural synthesis of ground surface textures. The generated texture samples are visually acceptable and have mean score of 2.45 with 0.1 standard deviation after 2K iteration. Also the discriminator loss of generated samples reached 0.74 at the final stage of training. The proposed framework can be used as an effective procedural texture synthesis framework for video game design and development.

In recent years, due to the rapid development of artificial intelligence (AI) and machine learning, its application has been widely used in many aspects of financial area, as well as significantly impacts financial market, institutions and regulation. The artificial intelligence technology brings enormous change to the entire financial industry, which creates a series of innovative financial services such as intelligent consultant, intelligent lending, monitoring and warning, and intelligent customer service as times required. In this paper, it aims to summarize the development and application of artificial intelligence and machine learning in financial system, as well as its impacts on macroeconomics and microeconomics. In the meantime, it is realised that a series of problems and risks were conducted by artificial intelligence during its use. Lastly, some suggestions and strategies are provided for reasonable usage of artificial intelligence in financial risk management, based on the financial risk management raised by artificial intelligence.

Purpose This study analyzes China’s strategic initiatives in metaverse and artificial intelligence (AI) development, examining their impact on academic research, industry innovation, and policy formulation. It aims to understand how government policies and investments have shaped research agendas and to identify challenges and opportunities in these fields. Design/methodology/approach The research employs a comprehensive analysis of government documents, funding schemes, and research output. It examines key policies, investment programs, and academic publications to track trends in metaverse and AI development in China. The study utilizes bibliometric analysis to assess publication trends, citation patterns, and international collaboration networks. Findings China’s proactive approach, characterized by strong government support and significant private sector investment, has led to a substantial increase in research output and quality in metaverse and AI fields. Chinese institutions have become major contributors to global publications, with growing citation rates and presence at international conferences. The research identifies emerging challenges in privacy, ethical AI development, and digital divide concerns. Practical implications The findings provide insights for policymakers, researchers, and industry stakeholders on the development trajectory of metaverse and AI technologies in China. They highlight the need for balanced approaches to innovation, regulation, and ethical considerations in these rapidly evolving fields. Social implications The study underscores the potential of metaverse and AI technologies to transform various sectors of society, from education and healthcare to entertainment and social interactions. It emphasizes the importance of addressing digital equity and ethical AI deployment to ensure broad societal benefits. Originality/value This research offers a comprehensive overview of China’s approach to metaverse and AI development, providing a unique perspective on the interplay between government initiatives, academic research, and industry innovation. It contributes to the broader discussion on the global development of these transformative technologies and their implications for future technological landscapes.

In order to design new methodologies for evaluating the user experience of video games, it is imperative to initially understand two core issues. Firstly, how are video games developed at present, including components such as processes, timescales and staff roles, and secondly, how do studios design and evaluate the user experience. This chapter will discuss the video game development process and the practices that studios currently use to achieve the best possible user experience. It will present four case studies from game developers Disney Interactive (Black Rock Studio), Relentless, Zoe Mode, and HandCircus, each detailing their game development process and also how this integrates with the user experience evaluation. The case studies focus on different game genres, platforms, and target user groups, ensuring that this chapter represents a balanced view of current practices in evaluating user experience during the game development process.

Game development has evolved to a point of being a method where it is being used to improve the day-to-day lifestyle of the person playing the game (henceforth referred to as a ‘player’). With these advancements, this paper represents the Integration of Artificial Intelligence (AI) in Game Development. AI has already been introduced in Game Development, ranging from Computer Generated Non-Player Characters (NPC), randomly generated maps or level, or even the difficulty of the level. In Gaming, AI is based on responsive and adaptive video game experiences. Using NPC to power interactive experience to simulate a human Game-player. One of the new Frontier of development and the method games are played, AI uses the behaviour of the player to control the game experience. Games refer to data that is made systematic using algorithms and helps to develop games with human interaction. As a developer, the experience needs to be delivered and AI makes the process easier. AI needs to store information to be able to reference which can respond to various stimuli. The mass of information required is the reason why AI has not been adapted into the Industry. Using Voice Intelligence is changing the inputs of games, and assist to increase intelligence genre games. Creating frameworks using design acknowledgment and reinforcement realizing is an important part of the future of Game development. By making the AI intelligent makes the games more real. Since Players pay attention to details, developers can use AI to help develop the game. In this paper, we will be focusing on the ways to identify the behaviour of the player and then using the information to develop the game further. By creating a profile, we can arrange and suit various reinforcements. Using the 16 personality tests, we can identify and rate the player based on the factor of their Mind, Energy, Nature, Tactics, and Identity.

The research relevance is determined by the importance of a thorough study of methods, schemes and models used by artificial intelligence to mechanise creativity in modern conditions of active technological development. The study aims to analyse the main processes taking place in modern art in connection with active technologization of work processes, to identify the leading concepts regarding the possibility of creating machine art in the future, etc. The employed methods are theoretical, such as analysis, systematisation, generalisation, etc., for studying key problems and further development of creativity based on artificial intelligence. The study examines in detail the main developments of Artificial General Intelligence and Artificial Narrow Intelligence, in particular the achievements of Generative adversarial networks and Creative adversarial networks. Artificial intelligence-generated art demonstrates the remarkable capabilities of technologies. The evolving artificial intelligence in the arts introduces “digital art”. Generative Adversarial Networks are used as a foundational tool for artists who use digital methods and texture generation to create unique compositions. Furthermore, sculptors collaborate with artificial intelligence tools to convert drawings into 3D models or transform historical art databases into sculptures. Creative thinking, a hallmark of human intelligence, is determined as artificial intelligence’s ability to generate new and original ideas. The development of emotional intelligence in artificial intelligence enables empathetic responses and the identification of human emotions through voice and facial expressions. The issues of authorised internationality, awareness of the creative process, psychological foundations of artificial empathy and emotional intelligence define the prospects for the development of neuroscience. Challenges persist in defining creativity, authorship, and legal aspects of artificial intelligence-generated art. The study materials may be useful for artists, art educators, technologists, and researchers interested in the intersection of technology and art, legal professionals (especially intellectual property law), and individuals involved in artificial intelligence development may find these findings valuable

This study analyses current advances and challenges of artificial intelligence (AI) development through a systematic literature review. The literature search was conducted across major databases including IEEE, ScienceDirect and Scopus. Peer-reviewed articles published between 2016-2023 were included based on relevance to the research topic. Out of 152 initial search results, 48 articles were selected for in-depth review using PRISMA guidelines. The results were analyzed to identify key focus areas, techniques and applications of AI over the selected period. The study found a significant increase in AI-related publications in 2022-2023 (62% of selected articles), indicating growing research interest. Key application areas identified are smart cities, education, image processing and medicine. Machine learning methods like neural networks and deep learning were frequently applied for tasks like classification, prediction and pattern recognition. Along with opportunities, ethical concerns like privacy, security, transparency and bias were major AI challenges discussed. Developing standards, regulations and testing mechanisms to ensure reliability and fairness of AI systems was highlighted. In summary, the systematic review demonstrates the rising significance of AI across industries, while underscoring the need to proactively address risks for its safe and ethical development. Overall, the study confirms that AI has great potential in various industries, but its implementation requires the development of ethical standards and data security to maximise benefits and minimise risks. The practical significance of the study is to deepen knowledge about AI and its impact on modern society.

Artificial intelligence technologies are developing intensively today, including due to the development of technologies of stable neural networks and cloud computing infrastructures, fuzzy system technologies, entropy administration, swarm intelligence, evolutionary computations, and many others. etc. At the same time, the problem of almost complete absence of normative legal regulation and normative technical regulation of the foundations, conditions and features of development, functioning and activities, integration into other systems and control over the use of artificial intelligence technologies is global today. The Article is devoted to the study of the specifics of the legal regulation of the use and development of artificial intelligence. Some approaches to the definition of artificial intelligence and the features of legal regulation of this sphere, which take place in the scientific literature, are considered and the author's definition of artificial intelligence is developed and given through the disclosure of its main features. In particular, according to the proposed definition, artificial intelligence is an artificial complex cybernetic computer-software-hardware system possessing the properties of substantivity, autonomy, and also the ability to perceive and analyze data, to self-learn. The question of positioning the artificial intelligence system as a special form of person (for example, the so-called «electronic person»), that is, granting it a certain legal personality depending on various factors and the field of application of such a system, is considered in this article. The article also notes the main possible approaches to legal support for the use and development of artificial intelligence systems, in particular, such approaches include the preemptive universal-total legal regulation and legal regulation aimed at the regulation of specific artificial intelligence systems. The main risks and uncertainties associated with artificial intelligence (and which are of great importance for the adoption of legislation in this field) have been investigated. There are drawn the conclusions about how to develop legislation on the use and development of artificial intelligence: consistently, taking into account the specifics of areas of its application, as well as balancing the interests of individuals, society and the state with regard to proper security and protection of individual rights, and interests related to the development of innovation for the benefit of the whole society.

This paper explains the ethical implications of artificial intelligence (AI) development towards achieving the level of artificial general intelligence (AGI) and analyzes the need for its social control. With the acceleration and intensification of AI growth and development, especially with the ongoing AI race, the transition from narrow AI to AGI becomes certain. The achievement of generative AI, which climaxes with chatbots (such as ChatGPT and others), transforms AI into a machine capable of creation. Although this AI application still appears relatively limited by algorithms, its learning ability is remarkable, and continuous advancements and the launch of increasingly sophisticated versions bring it ever closer to the AGI model. Each day brings us closer to that moment, which will signify AI’s transition from narrow AI to AGI. Unlike narrow AI, AGI deeply delves into the realm of ethics, and interpersonal and social relationships. Regulating AI-related policy and legally controlling AI represents one of the most serious and complex issues. In recent years, the community, led by corporate executives developing AI, prominent experts, researchers, scientists, writers, and other stakeholders, has made significant steps towards raising public awareness of the risks posed by advanced AI and making decisions, initiatives, and measures for monitoring, analyzing, and socially controlling the use of AI.

Current computer games are being set in increasingly more complex and dynamic virtual environments. Massively multiplayer online games, for example, are played in persistent virtual worlds, which evolve and change as players create and personalize their own virtual property. In contrast, technologies for controlling the behavior of nonplayer characters that populate virtual game worlds are frequently limited to preprogrammed rules. Characters using fixed rule-sets lack the ability to adapt in time with their environment. Motivated reinforcement learning offers an alternative to character design that can achieve nonplayer characters that both evolve and adapt in dynamic environments. This article presents and evaluates two computational models of motivation for use in nonplayer characters in persistent computer game worlds. These models represent motivation as an ongoing search for novelty, interest, and competence. Two metrics are introduced to evaluate the adaptability of characters controlled by motivated reinforcement learning agents using different models of motivation. These metrics characterize the behavior of nonplayer characters in terms of the variety and complexity of learned behaviors. An empirical evaluation of characters in simulated game scenarios shows that characters motivated by the search for competence are more adaptable in dynamic environments than those motivated by interest and novelty alone.

This article is dedicated to identifying the fundamental differences between artificial intelligence and machine learning. The aim of the research is to analyze, systematize, and improve the existing theoretical and methodological framework concerning the functioning of artificial intelligence and machine learning, as well as to define the distinctions between these systems. The article examines the current state and prospects for the development of artificial intelligence and machine learning as integral components of innovative technologies that facilitate the automation of complex processes and enhance efficiency across various fields. The research outlines the main stages in the development of artificial intelligence: weak artificial intelligence, which specializes in performing narrow tasks; strong artificial intelligence, aimed at achieving human cognitive capabilities; and superintelligence, which is expected to surpass human intelligence in many aspects. The study substantiates the role of machine learning as a key tool for implementing artificial intelligence, enabling the creation of systems that can self-learn and adapt to changing conditions without additional programming. The article provides examples of the application of artificial intelligence and machine learning in various sectors such as medicine, finance, cybersecurity, marketing, and transportation, where these technologies contribute to improving diagnostic processes, forecasting market fluctuations, and optimizing decision-making. In particular, the main advantages of machine learning are identified, including adaptability and the ability to make predictions based on large data sets, which enhances the effectiveness of analysis and decision-making in real time. The research into the development of artificial intelligence has revealed the technical and ethical challenges associated with creating strong artificial intelligence and superintelligence, which require the development of appropriate regulatory measures. The article emphasizes the significance of artificial intelligence and machine learning for modern society, their impact on various fields of economics, science, and technology, as well as the necessity for further research to ensure the safe and effective development of these technologies.

“Artificial intelligence” is one of the most popular buzzwords in the society at present, and was selected as the “Top Ten Chinese Media Popularity in 2017”. Human society is gradually entering a new era of artificial intelligence. Artificial intelligence is not just a scientific and technological innovation, but will bring about a big change in social life. As the State Council’s “New Generation Artificial Intelligence Development Plan” pointed out: “The rapid development of artificial intelligence will profoundly change human social life and change the world.” In the face of the new situation in the new era, governments at all levels must take the initiative to seek change and change, firmly grasp the major historical opportunities for the development of artificial intelligence, keep abreast of development, research and judge the general trend, actively plan, grasp the direction, seize the opportunities, and lead the world in the development of artificial intelligence. The trend, serving economic and social development and supporting national security, drives the overall leap and leapfrog development of national competitiveness.

Many computer games use custom scripts to control the ambient behaviors of non-player characters (NPCs). Therefore, a story writer must write fragments of computer code for the hundreds or thousands of NPCs in the game world. The challenge is to create entertaining and non-repetitive behaviors for the NPCs without investing substantial programming effort to write custom non-trivial scripts for each NPC. Current computer games have simplistic ambient behaviors for NPCs; it is rare for NPCs to interact with each other. In this paper, we describe how generative behavior patterns can be used to quickly and reliably generate ambient behavior scripts that are believable, entertaining and non-repetitive, even for the more difficult case of interacting NPCs. We demonstrate this approach using BioWare's Neverwinter Nights game.

Introduction: This research article describes anticipated directions of future artificial intelligence (AI) research and development and potential concerns and barriers associated with development as it relates to individuals with disabilities, particularly individuals who are visually impaired (i.e., those who are blind or have low vision). Methodology: A Delphi method was used to identify experts’ opinions and predictions around future AI developments focused around individuals who are blind or visually impaired. A combination of semistructured interviews and web-based surveys were utilized to identify consensus statements regarding the future of AI. Results: In the first round of semistructured interviews, 32 AI experts participated and shared their views on the future of AI and how it will affect individuals with disabilities. From the interviews, 72 common themes and insights were identified. Through 2 web-based surveys, the 72 common themes were narrowed down to 32 statements that reached consensus amongst the expert participants. The statement with the strongest support by the experts was: “A human in the loop is necessary for candidate screening”; which had a rating of 6.76 and a standard deviation of 0.44. The statement with the weakest support was: “AI-driven prenatal screening tools will create actions of eugenics”; which had a rating of 4.69 and a standard deviation of 0.95. Discussion: Experts discussed the careful balance between pushing AI development to support individuals’ lives and the potential harms of development without careful consideration. These findings point to a need for caution in the implementation of AI technologies and tools without proper dedication to fair representation and involvement of individuals with disabilities. Implications for Practitioners: Practitioners should acknowledge and explore the potential benefits that AI can provide for individuals with disabilities but also be aware of the bias and discrimination that is inherent in these tools.