Deduction of fighting game countermeasures using Neuroevolution of Augmenting Topologies

NeuroEvolution of augmenting topologies for solving a two-stage hybrid flow shop scheduling problem: A comparison of different solution strategies

Neuroevolution is a powerful and general technique for evolving the structure and weights of artificial neural networks. Though neuroevolutionary approaches such as NeuroEvolution of Augmenting Topologies (NEAT) have been successfully applied to various problems including classification, regression, and reinforcement learning problems, little work has explored application of these techniques to larger-scale multiclass classification problems. In this paper, NEAT is evaluated in several multiclass classification problems, and then extended via two ensemble approaches: One-vs-All and One-vs-One. These approaches decompose multiclass classification problems into a set of binary classification problems, in which each binary problem is solved by an instance of NEAT. These ensemble models exhibit reduced variance and increasingly superior accuracy as the number of classes increases. Additionally, higher accuracy is achieved early in training, even when artificially constrained for the sake of fair comparison with standard NEAT. However, because the approach can be trivially distributed, it can be applied quickly at large scale to solve real problems. In fact, these approaches are incorporated into DarwinTM, an enterprise automatic machine learning solution that also incorporates various other algorithmic enhancements to NEAT. The resulting complete system has proven robust to a wide variety of client datasets.

The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps regarding how to implement and best use AI to add value to mental healthcare services, providers, and consumers. The aim of this paper is to identify challenges and opportunities for AI use in mental healthcare and to describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. The paper is based on a selective review of articles concerning AI in mental healthcare and implementation science. Research in implementation science has established the importance of considering and planning for implementation from the start, the progression of implementation through different stages, and the appreciation of determinants at multiple levels. Determinant frameworks and implementation theories have been developed to understand and explain how different determinants impact on implementation. AI research should explore the relevance of these determinants for AI implementation. Implementation strategies to support AI implementation must address determinants specific to AI implementation in mental health. There might also be a need to develop new theoretical approaches or augment and recontextualize existing ones. Implementation outcomes may have to be adapted to be relevant in an AI implementation context. Knowledge derived from implementation science could provide an important starting point for research on implementation of AI in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research. However, when taking advantage of the existing knowledge basis, it is important to also be explorative and study AI implementation in health and mental healthcare as a new phenomenon in its own right since implementing AI may differ in various ways from implementing evidence-based practices in terms of what implementation determinants, strategies, and outcomes are most relevant.Plain Language Summary: The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps concerning how to implement and best use AI to add value to mental healthcare services, providers, and consumers. This paper is based on a selective review of articles concerning AI in mental healthcare and implementation science, with the aim to identify challenges and opportunities for the use of AI in mental healthcare and describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. AI offers opportunities for identifying the patients most in need of care or the interventions that might be most appropriate for a given population or individual. AI also offers opportunities for supporting a more reliable diagnosis of psychiatric disorders and ongoing monitoring and tailoring during the course of treatment. However, AI implementation challenges exist at organizational/policy, individual, and technical levels, making it relevant to draw on implementation science knowledge for understanding and facilitating implementation of AI in mental healthcare. Knowledge derived from implementation science could provide an important starting point for research on AI implementation in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research.

We used multiobjective genetic algorithms with neuroevolution of augmenting topologies (NEAT) to evolve effective micro behaviors for opposing groups with heterogeneous compositions in StarCraft II, an RTS game. We used the Fast Nondominated Sorting Genetic Algorithm to maximize damage done and minimize damage received in a skirmish, and used this two objective fitness to guide NEAT to evolve the structure and weights of a neural network based controller. The evolved NEAT network controls the movement and attack commands for each unit. We show that non-dominated selection and NEAT can be used together to generate effective micro for groups with two types or three types of units on each side. The evolved micro also generalized well to random configurations, doing well along both objectives. We also manually co-evolved against the best performing individuals produced during a run for multiple cycles and show that this improves micro resulting in better performance against the default Starcraft II AI.

Opponent models are necessary in games where the game state is only partially known to the player, since the player must infer the state of the game based on the opponents actions. This paper presents an architecture and a process for developing neural network game players that utilize explicit opponent models in order to improve game play against unseen opponents. The model is constructed as a mixture over a set of cardinal opponents, i.e. opponents that represent maximally distinct game strategies. The model is trained to estimate the likelihood that the opponent will make the same move as each of the cardinal opponents would in a given game situation. Experiments were performed in the game of Guess It, a simple game of imperfect information that has no optimal strategy for defeating specific opponents. Opponent modeling is therefore crucial to play this game well. Both opponent modeling and game-playing neural networks were trained using NeuroEvolution of Augmenting Topologies (NEAT). The results demonstrate that game-playing provided with the model outperform networks not provided with the model when played against the same previously unseen opponents. The cardinal mixture architecture therefore constitutes a promising approach for general and dynamic opponent modeling in game-playing.

Globalisation of the strategy for the development and implementation of artificial intelligence (AI) in Ukraine and/or deepening the direction in nominal areas of social life, i.e. individuals in society, respectively, in the fields of art, design, medicine, commercial activity, science and education, banking and deep military and national strategy and national security in cadence formats such as ChatGPT, i.e: • Artificial intelligence – AI; • Artificial Neural Networks – ANN – computing systems. The dynamic position in the context of the emergence of artificial intelligence (AI) and its further application in the educational process in relation to the first stage, i.e. the species concept on the part of educators and students of education from among the convicts in the status of a special contingent, respectively, was remade in relation to the nominal field of vocational training in the context of penitentiary education, In other words, artificial intelligence (AI) in the form of a contact line requested a penitentiary trajectory for the format of the social and creative model teacher-education student from among prisoners in the status of a special contingent. Accordingly, the special contingent in the form of students from the prison population in the status of a special contingent needs to be fluent in digital technologies in accordance with the requirements of modern society, to improve and enhance the nominal abilities that will be needed for further employment in the digital world. The motivation of the present regarding the project perspective of artificial intelligence (AI) in penitentiary education in the status of a special contingent, that is, the involvement of the social and creative model of teacher-educator from among the convicts of imprisonment in the status of a special contingent is characterized by the intense development of artificial intelligence (AI) technologies and the introduction of. The generation of the relationship between artificial intelligence (AI) and education goes beyond the application of artificial intelligence (AI) during the acquisition of theoretical training, i.e., taking into account the study of artificial intelligence (AI) technologies and the professional training of students from among prisoners of imprisonment in the status of a special contingent within the framework of penitentiary education.

ObjectivesTo explore the views of intensive care professionals in high-income countries (HICs) and lower-to-middle-income countries (LMICs) regarding the use and implementation of artificial intelligence (AI) technologies in intensive care units...

Artificial intelligence and numerical models in hybrid renewable energy systems with fuel cells: Advances and prospects

Artificial intelligence (AI) implementation within the National Health Service (NHS): the South West London AI Working Group experience

Artificial Intelligence (AI) became a concern of many scholars including Educators. Some educators believed that AI can be a very sophisticated tool to support teaching and learning processes, but others believed otherwise. Discussion on Implementation of AI in the field of education always stimulates the appearance of pros and cons. This study tries to explore secondary school teacher perspectives on the use of Artificial Intelligence in the field of education. This study explores 15 teachers of secondary school in Malang. In-depth interview was taken to get comprehensive data. The result of this study showed that there are 5 AI tools which became frequently used by teachers, those are: ChatGPT, Grammarly, Canva, Gamma, and Class point. Interestingly, 42% of the informants are using AI for professional purposes like supporting their teaching and learning process. Besides, 33% of the informants are using it for academic purposes like writing the article, news, or other tasks which are able to improve their academic competences. There were also 25% of the informants were used AI for personal purposes which is not correlated to their profession as teachers, like for having fun, trial and error, or out of curiosity. Moreover, the use of Artificial Intelligence in the field of education created two-side of perspectives: pro and con. The pro argued that AI can be fully adapted in secondary school for its numerous benefits for both teacher and student, while the con argued that AI would have many problems if it was implemented. There are four arguments behind the pros: the use of AI in education proved that teachers are adaptive towards newest technology, it is also able to improve teacher skills and competencies, it also can support teacher’s administrative tasks, and students need to be introduced to the latest technology for their future. On the other side, there are three arguments behind the cons: ethical issues, lack of learning process, and technology dependencies and loss of human interaction.

Introduction: Artificial intelligence (AI) is the fascinating result of the convergence of various technologies, algorithms and approaches. Its role in early detection and diagnosis will be a boon to society in the next few years and should be seen as 'Augmented Intelligence.' Issues like ethical considerations and collaborations need to be sorted out to ensure apt implementation in healthcare. This article reviews the current status of AI in developing nations like India and highlights some insights that could provide new directions and opportunities for AI in healthcare, especially in the field of radiology. Implications for Patient Care: (1) AI will aid in nation-wide research and cooperation that will provide an impetus for the development of imaging science and decentralization of medical services. (2) AI may help to bridge the gap for need of specialized medical personnel in the peripheral areas in developing countries like India. (3) Government initiatives, ethical considerations and joint public private sector collaborations will ensure smooth transition and implementation of AI in healthcare especially in radiology. Summary Statement: This manuscript highlights the existing gap between the developed and developing nations regarding implementation and acceptance of AI in healthcare and at the same time brings out insights in regard to the government initiatives to ease the progress of AI in healthcare in developing countries like India.

Artificial intelligence (AI) has successfully made its way into contemporary industrial sectors such as automobiles, defense, industrial automation 4.0, healthcare technologies, agriculture, and many other domains because of its ability to act autonomously without continuous human interventions. However, this capability requires processing huge amounts of learning data to extract useful information in real time. The buzz around AI is not new, as this term has been widely known for the past half century. In the 1960s, scientists began to think about machines acting more like humans, which resulted in the development of the first natural language processing computers. It laid the foundation of AI, but there were only a handful of applications until the 1990s due to limitations in processing speed, memory, and computational power available. Since the 1990s, advancements in computer architecture and memory organization have enabled microprocessors to deliver much higher performance. Simultaneously, improvements in the understanding and mathematical representation of AI gave birth to its subset, referred to as machine learning (ML). ML includes different algorithms for independent learning, and the most promising ones are based on brain-inspired techniques classified as artificial neural networks (ANNs). ANNs have subsequently evolved to have deeper and larger structures and are often characterized as deep neural networks (DNN) and convolution neural networks (CNN). In tandem with the emergence of multicore processors, ML techniques started to be embedded in a range of scenarios and applications. Recently, application-specific instruction-set architecture for AI applications has also been supported in different microprocessors. Thus, continuous improvement in microprocessor capabilities has reached a stage where it is now possible to implement complex real-time intelligent applications like computer vision, object identification, speech recognition, data security, spectrum sensing, etc. This paper presents an overview on the evolution of AI and how the increasing capabilities of microprocessors have fueled the adoption of AI in a plethora of application domains. The paper also discusses the upcoming trends in microprocessor architectures and how they will further propel the assimilation of AI in our daily lives.

In today's rapidly evolving business landscape, Artificial Intelligence (AI) stands as the proverbial 'elephant in the room,' profoundly shaping diverse sectors and contexts. While debates rage among policymakers, practitioners, and politicians about regulating AI's widespread use, it's undeniable that AI represents a long-awaited digital technology poised to revolutionize organizational performance. Amidst the post-COVID era, the allure of AI has intensified, yet the critical question lingers: Can firms effectively harness AI and other technologies, such as Generative AI and Large Language Models (LLMs), to enhance their existing systems? Through a systematic literature review, a clear correlation between firms’ implementation of AI, including the cutting-edge Generative AI, and their ability to adapt to changing market dynamics, drive operational excellence, and unlock new avenues for growth are explored. Moreover, key drivers of AI and digital adoption, such as the imperative for data-driven decision-making, the quest for customer-centricity, and the drive for sustainable business practices are explored. Our research not only highlights the transformative potential of AI and digital technologies but also provides actionable insights for business leaders navigating the complexities of technology adoption. By understanding the motivations, challenges, and strategic imperatives driving firms' technology choices, including the integration of Generative AI and LLMs, organizations can chart a path towards sustainable growth and competitive advantage in the digital age. This study underscores the revolutionary impact of Generative AI in digital transformation, offering a comprehensive understanding of their role in shaping the future of business.

To evaluate and understand pregnant patients' perspectives on the implementation of artificial intelligence (AI) in clinical care with a focus on opportunities to improve healthcare technologies and healthcare delivery. We developed an anonymous survey and enrolled patients presenting to the labor and delivery unit at a tertiary care center September 2019-June 2020. We investigated the role and interplay of patient demographic factors, healthcare literacy, understanding of AI, comfort levels with various AI scenarios, and preferences for AI use in clinical care. Of the 349 parturients, 57.6% were between the ages of 25-34years, 90.1% reported college or graduate education and 69.2% believed the benefits of AI use in clinical care outweighed the risks. Cluster analysis revealed 2 distinct groups: patients more comfortable with clinical AI use (Pro-AI) and those who preferred physician presence (AI-Cautious). Pro-AI patients had a higher degree of education, were more knowledgeable about AI use in their daily lives and saw AI use as a significant advancement in medicine. AI-Cautious patients reported a lack of human qualities and low trust in the technology as detriments to AI use. Patient trust and the preservation of the human physician-patient relationship are critical in moving forward with AI implementation in healthcare. Pregnant individuals are cautiously optimistic about AI use in their care. Our findings provide insights into the status of AI use in perinatal care and provide a platform for driving patient-centered innovations.

In the context of rapid digital transformation, artificial intelligence (AI) technologies implementation is becoming crucial for e-commerce competitiveness. According to Eurostat, in 2024, 13.48% of EU enterprises use AI technologies, showing growth from 8.0% in 2023. Given Ukraine's European integration aspirations, there is a need to study and adapt EU experience in implementing AI in e-commerce. The research aim is to analyze transformation processes in e-commerce systems under AI technologies influence, study EU experience, and identify development directions for Ukraine. Methodology is based on a systematic approach combining: analysis of relationships between e-commerce components, statistical analysis of quantitative data, comparative analysis of EU and Ukraine indicators, and economic modeling to assess AI implementation effects. Analysis revealed differentiation in AI implementation across the EU by enterprise size (from 11.21% for small to 41.17% for large companies) and countries (leaders - Denmark (27.58%), Sweden (25.09%), Belgium (24.71%)). In Ukraine, AI implementation is 6.9%. Priority areas identified: text processing (26.9%), speech generation (21.2%), and speech recognition (18.7%). Recommendations for phased AI implementation were developed considering enterprise size and industry specifics, with three implementation horizons and differentiated approach. The recommendations can be used for e-commerce enterprises' digital transformation strategies and state support programs. The proposed adaptation mechanisms optimize AI implementation and enhance Ukrainian enterprises' competitiveness. Further research will focus on industry-specific AI implementation strategies and their economic efficiency evaluation in European integration context.