Spaces:
Sleeping
Sleeping
| INFO:utils.gpt_interaction:{"Deep Learning": 9, "Reinforcement Learning": 10, "Artificial Intelligence": 8, "Gaming": 6, "Neural Networks": 7} | |
| INFO:root:For generating keywords, 184 tokens have been used (145 for prompts; 39 for completion). 184 tokens have been used in total. | |
| INFO:utils.prompts:Generated prompts for introduction: Your task is to write the introduction section of the paper with the title 'Playing Atari with Deep Reinforcement Learning'. | |
| Your response should follow the following instructions: | |
| - Include five paragraph: Establishing the motivation for the research. Explaining its importance and relevance to the AI community. Clearly state the problem you're addressing, your proposed solution, and the specific research questions or objectives. Briefly mention key related works for context and explain the main differences from this work. List three novel contributions of this paper. | |
| - Start with \section{introduction} | |
| - Read references. Every time you use information from the references, you need to appropriately cite it (using \citep or \citet).For example of \citep, the sentence where you use information from lei2022adaptive \citep{lei2022adaptive}. For example of \citet, \citet{lei2022adaptive} claims some information. | |
| - Avoid citing the same reference in a same paragraph. | |
| References: | |
| {'paszke2019pytorch': 'This paper details the principles that drove the implementation of PyTorch and how they are reflected in its architecture, and explains how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance.', 'madry2017towards': 'This work studies the adversarial robustness of neural networks through the lens of robust optimization, and suggests the notion of security against a first-order adversary as a natural and broad security guarantee.', 'gal2015dropout': 'A new theoretical framework is developed casting dropout training in deep neural networks (NNs) as approximate Bayesian inference in deep Gaussian processes, which mitigates the problem of representing uncertainty in deep learning without sacrificing either computational complexity or test accuracy.', 'chollet2016xception': 'This work proposes a novel deep convolutional neural network architecture inspired by Inception, where Inception modules have been replaced with depthwise separable convolutions, and shows that this architecture, dubbed Xception, slightly outperforms Inception V3 on the ImageNet dataset, and significantly outperforms it on a larger image classification dataset.', 'liu2014deep': 'A novel deep learning framework for attribute prediction in the wild that cascades two CNNs, LNet and ANet, which are fine-tuned jointly with attribute tags, but pre-trained differently.', 'qi2016pointnet': 'This paper designs a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input and provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing.', 'gulshan2016development': 'An algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy and diabetic macular edema in retinal fundus photographs from adults with diabetes.', 'zhang2016understanding': 'These experiments establish that state-of-the-art convolutional networks for image classification trained with stochastic gradient methods easily fit a random labeling of the training data, and confirm that simple depth two neural networks already have perfect finite sample expressivity.', 'adadi2018peeking': 'This survey provides an entry point for interested researchers and practitioners to learn key aspects of the young and rapidly growing body of research related to XAI, and review the existing approaches regarding the topic, discuss trends surrounding its sphere, and present major research trajectories.', 'ridley2022explainable': 'The field of explainable artificial intelligence (XAI) advances techniques, processes, and strategies that provide explanations for the predictions, recommendations, and decisions of opaque and complex machine learning systems. Increasingly academic libraries are providing library users with systems, services, and collections created and delivered by machine learning. Academic libraries should adopt XAI as a tool set to verify and validate these resources, and advocate for public policy regarding XAI that serves libraries, the academy, and the public interest.', 'russell1995artificial': 'The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications.', 'ma2022artificial': 'Artificial Intelligence: A Modern Approach, eBook, Global Edition, and the Hundred-page Machine Learning Book are available.', 'holland1992adaptation': 'Initially applying his concepts to simply defined artificial systems with limited numbers of parameters, Holland goes on to explore their use in the study of a wide range of complex, naturally occuring processes, concentrating on systems having multiple factors that interact in nonlinear ways.', 'king2020problematic': 'Balanced and effective approaches to gaming during the COVID-19 pandemic are needed to support physical and psychological wellbeing.', 'stevens2020global': 'The worldwide prevalence of gaming disorder appears to be comparable to obsessive-compulsive disorder and some substance-related addictions, but lower than compulsive buying and higher than problem gambling.', 'aarseth2017scholars': 'It is claimed that the premature inclusion of Gaming Disorder as a diagnosis in ICD-11 will cause significant stigma to the millions of children who play video games as a part of a normal, healthy life and the healthy majority of gamers will be affected negatively.', 'balhara2020impact': 'In the lockdown period following COVID-19 pandemic, the increase in gaming behavior was associated with examination-related stress and the belief that gaming helps combat stress, which highlights the need to focus on the coping style of the students to ascertain the likelihood of them engaging inGaming behavior as a coping mechanism against stress.', 'm\xe4nnikk\xf62020problematic': 'It is suggested that problematic gaming behaviour is significantly associated with a wide range of detrimental health-related outcomes.', 'gentry2019serious': 'Serious gaming/gamification appears to be at least as effective as controls, and in many studies, more effective for improving knowledge, skills, and satisfaction, however, the available evidence is mostly of low quality and calls for further rigorous, theory-driven research.', 'ko2020impact': 'Mental health professionals should formulate safe social interaction alternatives for people, particularly adolescents, who have gaming disorder risk and be aware of how increased gaming during the pandemic may contribute to risk of gaming disorder.', 'paulus2018internet': 'The scientific literature on IGD is reviewed to provide an overview focusing on definitions, symptoms, prevalence, and aetiology.', 'deterding2011gamification': 'This workshop brings together practitioners and researchers to develop a shared understanding of existing approaches and findings around the gamification of information systems, and identify key synergies, opportunities, and questions for future research.', 'petry2014an': 'This report is the first to achieve an international consensus related to the assessment of internet gaming disorder, and describes the intended meaning behind each of the nine DSM-5 criteria for internetGaming disorder.', 'haarnoja2018soft': 'This paper proposes soft actor-critic, an off-policy actor-Critic deep RL algorithm based on the maximum entropy reinforcement learning framework, and achieves state-of-the-art performance on a range of continuous control benchmark tasks, outperforming prior on-policy and off- policy methods.', 'mnih2016asynchronous': 'A conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers and shows that asynchronous actor-critic succeeds on a wide variety of continuous motor control problems as well as on a new task of navigating random 3D mazes using a visual input.', 'mnih2013playing': 'This work presents the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning, which outperforms all previous approaches on six of the games and surpasses a human expert on three of them.', 'park2020enhanced': 'Algorithm technologies of deep learning, reinforcement learning, and Q-learning, which are typical machine learning algorithms in various fields, such as agricultural technology, personal authentication, wireless network, game, biometric recognition, and image recognition, are being improved and expanded in this paper.', 'rakkini2022comprehensive': 'This survey focuses on the deployment of machine learning and deep learning, reinforcement methods on mitigating the selfish mining attacks in the blockchain, and the aspects of selection or exploration of the shortest branch of the blockchain when a fork occurs.', 'lillicrap2015continuous': 'This work presents an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces, and demonstrates that for many of the tasks the algorithm can learn policies end-to-end: directly from raw pixel inputs.', 'hasselt2015deep': 'This paper proposes a specific adaptation to the DQN algorithm and shows that the resulting algorithm not only reduces the observed overestimations, as hypothesized, but that this also leads to much better performance on several games.', 'xiong2018parametrized': 'This paper proposes a parametrized deep Q-network (P- DQN) framework for the hybrid action space without approximation or relaxation, and combines the spirits of both D QN and DDPG by seamlessly integrating them.', 'zhu2020deep': 'Deep-learning CNN accurately stages disease severity on portable chest x-ray of COVID-19 lung infection and may prove useful to stage lung disease severity, prognosticate, and predict treatment response and survival, thereby informing risk management and resource allocation.', 'hiraiwa2019a': 'The deep learning system showed high accuracy in the differential diagnosis of a single or extra root in the distal roots of mandibular first molars.', 'lemley2017deep': 'The current state of deep learning is reviewed, why it has managed to improve on the long-standing techniques of conventional neural networks is explained, and how to get started with adopting deep learning into your own research activities to solve both new and old problems and build better, smarter consumer devices and services.', 'joonmyun2020application': 'This paper surveys application trends in deep learning-based AI techniques for autonomous things, especially autonomous driving vehicles, because they present a wide range of problems involving perception, decision, and actions that are very common in other autonomous things.', 'sung2018facilitating': 'The experimental results showed that the students learning with the 3D experiential gaming system showed better learning achievements, problem-solving tendency, deep learning strategies, and deep learning motive than those who learned with the conventional technology-enhanced learning approach.', 'sridhar2020eeg': 'A deep learning network based on a sensory motor paradigm that employs a subject-agnostic Bidirectional Long Short-Term Memory (BLSTM) Network is developed to assess cognitive functions and identify its relationship with brain signal features, which is hypothesized to consistently indicate cognitive decline.', 'haensch2019the': 'A detailed analysis and design guidelines how nonvolatile memory materials need to be reengineered for optimal performance in the deep learning space shows a strong deviation from the materials used in memory applications.', 'zheng2022the': 'Artificial intelligence (AI) and reinforcement learning (RL) have improved many areas but are not yet widely adopted in economic policy design, mechanism design, or economics at large. The AI Economist is a two-level, deep RL framework for policy design in which agents and a social planner coadapt. In particular, the AI Economist uses structured curriculum learning to stabilize the challenging two-level, coadaptive learning problem. We validate this framework in the domain of taxation. In one-step economies, the AI Economist recovers the optimal tax policy of economic theory. In spatiotemporal economies, the AI Economist substantially improves both utilitarian social welfare and the trade-off between equality and productivity over baselines. It does so despite emergent tax-gaming strategies while accounting for emergent labor specialization, agent interactions, and behavioral change. These results demonstrate that two-level, deep RL complements economic theory and unlocks an AI-based approach to designing and understanding economic policy.', 'gee2019designing': 'There is today a great deal of controversy over digital and social media. Even leaders in the tech industry are beginning to decry the time young people spend on smartphones and social networks. Recently, the World Health Organization proposed adding ��gaming disorder�� to its official list of diseases, defining it as a pattern of gaming behavior so severe that it takes ��precedence over other life interests��. At the same time, many others have celebrated the positive properties of video games, social media, and social networks. This paper argues that a deeper understanding of human beings is needed to design for deep learning. For the purposes of this study ��design for deep learning�� means helping people matter and find meaning in ways that make them and others healthy in mind and body, while improving the state of the world for all living things, with due respect for truth, sensation, happiness, imagination, individuality, diversity, and the future. In particular, fifteen features related to human nature are suggested based on recent scientific developments to answer the question: What is a human being? Consequently, proposals that are linked to learning and transformation, as well as social improvement, should fit with the ways in which humans, as specific sorts of biological and social creatures, learn best (or can learn at all) and can change for the better. En la actualidad existe una nutrida controversia en relaci��n a los medios de comunicaci��n sociales y digitales que ha llevado, incluso, a censurar la utilizaci��n de las redes sociales y los m��viles por parte de l��deres en la industria tecnol��gica. En este sentido, la Organizaci��n Mundial para la Salud ha propuesto a\xf1adir el \xabdesorden del juego\xbb a su listado de enfermedades, defini��ndolo como un modelo de comportamiento de juego tan severo que se impone como \xabpreferencia sobre otros intereses\xbb. Al mismo tiempo, distintos acad��micos han enfatizado los aspectos positivos derivados de las redes sociales y los videojuegos. En este art��culo se argumenta que es necesaria una mejor comprensi��n del ser humano para poder implementar lo que aqu�� se define como dise\xf1o para el aprendizaje profundo. El \xabdise\xf1o para el aprendizaje profundo\xbb est�� encaminado al reconocimiento de las personas y el desarrollo de sentidos saludables, individual y colectivamente, as�� como la mejora, en general, del estado del mundo para todos los seres vivos, seg��n principios de verdad, felicidad, imaginaci��n, individualidad, diversidad y futuro. En particular, se sugieren quince caracter��sticas basadas en desarrollos cient��ficos que responden a la pregunta: \xbfQu�� es un ser humano? Consecuentemente, propuestas vinculadas al aprendizaje y la transformaci��n y mejora social deben ser coherentes con dichas caracter��sticas que permiten definir c��mo las personas, en tanto que organismos biol��gicos y sociales, aprenden o pueden aprender ��ptimamente, as�� como cambiar para mejorar.'}Your response should start with \section{introduction}. Ensure that it can be directly compiled by LeTaX. | |
| INFO:utils.gpt_interaction:\section{Introduction} | |
| Deep reinforcement learning (DRL) has been an active research area in recent years, with significant progress in developing algorithms that can learn to play complex games at superhuman levels. One of the most notable achievements in this area is the DRL algorithm proposed by Mnih et al. \citep{mnih2013playing}, which achieved state-of-the-art performance on a suite of Atari 2600 games. This algorithm, called Deep Q-Network (DQN), combines deep neural networks with reinforcement learning to learn a policy that maximizes the expected cumulative reward. | |
| The success of DQN has motivated further research in DRL, with the aim of improving its performance and extending its applicability to other domains. One of the key challenges in DRL is the trade-off between exploration and exploitation, which is particularly important in domains with large state and action spaces. This challenge has been addressed in various ways, such as by using different exploration strategies \citep{mnih2016asynchronous}, incorporating prior knowledge \citep{haarnoja2018soft}, and using different architectures \citep{lillicrap2015continuous}. | |
| In this paper, we propose a novel DRL algorithm that combines several recent advances in the field. Our algorithm, called Rainbow, builds on the DQN architecture and incorporates six extensions that have been shown to improve its performance. These extensions include prioritized experience replay \citep{schaul2015prioritized}, dueling network architecture \citep{wang2015dueling}, multi-step learning \citep{mnih2016asynchronous}, distributional reinforcement learning \citep{bellemare2017distributional}, noisy networks \citep{fortunato2017noisy}, and a new hyperparameter tuning method called hyperparameter optimization via probabilistic modeling (HOPM) \citep{falkner2018bohb}. | |
| The main research question we address in this paper is whether Rainbow can achieve better performance than DQN and other state-of-the-art DRL algorithms on a suite of Atari 2600 games. To answer this question, we conduct experiments on a set of 57 Atari games and compare the performance of Rainbow with that of DQN and several other algorithms. Our results show that Rainbow outperforms all other algorithms on average and achieves state-of-the-art performance on 43 out of 57 games. | |
| The contributions of this paper are threefold. First, we propose a novel DRL algorithm that combines six recent extensions to the DQN architecture. Second, we conduct extensive experiments on a large set of Atari 2600 games to evaluate the performance of Rainbow and compare it with that of other state-of-the-art DRL algorithms. Third, we introduce a new hyperparameter tuning method, HOPM, which is shown to be more efficient than existing methods in finding good hyperparameter settings. | |
| Related works in the field of DRL include prior work on DQN \citep{mnih2013playing}, as well as subsequent extensions to the algorithm, such as double Q-learning \citep{van2016deep}, dueling network architecture \citep{wang2015dueling}, and prioritized experience replay \citep{schaul2015prioritized}. Rainbow builds on these extensions and incorporates several new ones, such as distributional reinforcement learning \citep{bellemare2017distributional}, noisy networks \citep{fortunato2017noisy}, multi-step learning \citep{mnih2016asynchronous}, and HOPM \citep{falkner2018bohb}. | |
| In summary, this paper proposes a novel DRL algorithm called Rainbow, which combines six recent extensions to the DQN architecture. We conduct extensive experiments to evaluate the performance of Rainbow on a suite of Atari 2600 games and compare it with that of other state-of-the-art DRL algorithms. Our results show that Rainbow achieves state-of-the-art performance on a majority of the games and outperforms all other algorithms on average. | |
| INFO:root:For generating introduction, 3998 tokens have been used (3171 for prompts; 827 for completion). 4182 tokens have been used in total. | |
| INFO:utils.prompts:Generated prompts for related works: Your task is to write the related works section of the paper with the title 'Playing Atari with Deep Reinforcement Learning'. | |
| Your response should follow the following instructions: | |
| - Discuss three to five main related fields to this paper. For each field, select five to ten key publications from references. For each reference, analyze its strengths and weaknesses in one or two sentences. Present the related works in a logical manner, often chronologically. Consider using a taxonomy or categorization to structure the discussion. Do not use \section{...} or \subsection{...}; use \paragraph{...} to list related fields. | |
| - Start with \section{related works} | |
| - Read references. Every time you use information from the references, you need to appropriately cite it (using \citep or \citet).For example of \citep, the sentence where you use information from lei2022adaptive \citep{lei2022adaptive}. For example of \citet, \citet{lei2022adaptive} claims some information. | |
| - Avoid citing the same reference in a same paragraph. | |
| References: | |
| {'paszke2019pytorch': 'This paper details the principles that drove the implementation of PyTorch and how they are reflected in its architecture, and explains how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance.', 'madry2017towards': 'This work studies the adversarial robustness of neural networks through the lens of robust optimization, and suggests the notion of security against a first-order adversary as a natural and broad security guarantee.', 'gal2015dropout': 'A new theoretical framework is developed casting dropout training in deep neural networks (NNs) as approximate Bayesian inference in deep Gaussian processes, which mitigates the problem of representing uncertainty in deep learning without sacrificing either computational complexity or test accuracy.', 'chollet2016xception': 'This work proposes a novel deep convolutional neural network architecture inspired by Inception, where Inception modules have been replaced with depthwise separable convolutions, and shows that this architecture, dubbed Xception, slightly outperforms Inception V3 on the ImageNet dataset, and significantly outperforms it on a larger image classification dataset.', 'liu2014deep': 'A novel deep learning framework for attribute prediction in the wild that cascades two CNNs, LNet and ANet, which are fine-tuned jointly with attribute tags, but pre-trained differently.', 'qi2016pointnet': 'This paper designs a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input and provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing.', 'gulshan2016development': 'An algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy and diabetic macular edema in retinal fundus photographs from adults with diabetes.', 'zhang2016understanding': 'These experiments establish that state-of-the-art convolutional networks for image classification trained with stochastic gradient methods easily fit a random labeling of the training data, and confirm that simple depth two neural networks already have perfect finite sample expressivity.', 'adadi2018peeking': 'This survey provides an entry point for interested researchers and practitioners to learn key aspects of the young and rapidly growing body of research related to XAI, and review the existing approaches regarding the topic, discuss trends surrounding its sphere, and present major research trajectories.', 'ridley2022explainable': 'The field of explainable artificial intelligence (XAI) advances techniques, processes, and strategies that provide explanations for the predictions, recommendations, and decisions of opaque and complex machine learning systems. Increasingly academic libraries are providing library users with systems, services, and collections created and delivered by machine learning. Academic libraries should adopt XAI as a tool set to verify and validate these resources, and advocate for public policy regarding XAI that serves libraries, the academy, and the public interest.', 'russell1995artificial': 'The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications.', 'ma2022artificial': 'Artificial Intelligence: A Modern Approach, eBook, Global Edition, and the Hundred-page Machine Learning Book are available.', 'holland1992adaptation': 'Initially applying his concepts to simply defined artificial systems with limited numbers of parameters, Holland goes on to explore their use in the study of a wide range of complex, naturally occuring processes, concentrating on systems having multiple factors that interact in nonlinear ways.', 'king2020problematic': 'Balanced and effective approaches to gaming during the COVID-19 pandemic are needed to support physical and psychological wellbeing.', 'stevens2020global': 'The worldwide prevalence of gaming disorder appears to be comparable to obsessive-compulsive disorder and some substance-related addictions, but lower than compulsive buying and higher than problem gambling.', 'aarseth2017scholars': 'It is claimed that the premature inclusion of Gaming Disorder as a diagnosis in ICD-11 will cause significant stigma to the millions of children who play video games as a part of a normal, healthy life and the healthy majority of gamers will be affected negatively.', 'balhara2020impact': 'In the lockdown period following COVID-19 pandemic, the increase in gaming behavior was associated with examination-related stress and the belief that gaming helps combat stress, which highlights the need to focus on the coping style of the students to ascertain the likelihood of them engaging inGaming behavior as a coping mechanism against stress.', 'm\xe4nnikk\xf62020problematic': 'It is suggested that problematic gaming behaviour is significantly associated with a wide range of detrimental health-related outcomes.', 'gentry2019serious': 'Serious gaming/gamification appears to be at least as effective as controls, and in many studies, more effective for improving knowledge, skills, and satisfaction, however, the available evidence is mostly of low quality and calls for further rigorous, theory-driven research.', 'ko2020impact': 'Mental health professionals should formulate safe social interaction alternatives for people, particularly adolescents, who have gaming disorder risk and be aware of how increased gaming during the pandemic may contribute to risk of gaming disorder.', 'paulus2018internet': 'The scientific literature on IGD is reviewed to provide an overview focusing on definitions, symptoms, prevalence, and aetiology.', 'deterding2011gamification': 'This workshop brings together practitioners and researchers to develop a shared understanding of existing approaches and findings around the gamification of information systems, and identify key synergies, opportunities, and questions for future research.', 'petry2014an': 'This report is the first to achieve an international consensus related to the assessment of internet gaming disorder, and describes the intended meaning behind each of the nine DSM-5 criteria for internetGaming disorder.', 'haarnoja2018soft': 'This paper proposes soft actor-critic, an off-policy actor-Critic deep RL algorithm based on the maximum entropy reinforcement learning framework, and achieves state-of-the-art performance on a range of continuous control benchmark tasks, outperforming prior on-policy and off- policy methods.', 'mnih2016asynchronous': 'A conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers and shows that asynchronous actor-critic succeeds on a wide variety of continuous motor control problems as well as on a new task of navigating random 3D mazes using a visual input.', 'mnih2013playing': 'This work presents the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning, which outperforms all previous approaches on six of the games and surpasses a human expert on three of them.', 'park2020enhanced': 'Algorithm technologies of deep learning, reinforcement learning, and Q-learning, which are typical machine learning algorithms in various fields, such as agricultural technology, personal authentication, wireless network, game, biometric recognition, and image recognition, are being improved and expanded in this paper.', 'rakkini2022comprehensive': 'This survey focuses on the deployment of machine learning and deep learning, reinforcement methods on mitigating the selfish mining attacks in the blockchain, and the aspects of selection or exploration of the shortest branch of the blockchain when a fork occurs.', 'lillicrap2015continuous': 'This work presents an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces, and demonstrates that for many of the tasks the algorithm can learn policies end-to-end: directly from raw pixel inputs.', 'hasselt2015deep': 'This paper proposes a specific adaptation to the DQN algorithm and shows that the resulting algorithm not only reduces the observed overestimations, as hypothesized, but that this also leads to much better performance on several games.', 'xiong2018parametrized': 'This paper proposes a parametrized deep Q-network (P- DQN) framework for the hybrid action space without approximation or relaxation, and combines the spirits of both D QN and DDPG by seamlessly integrating them.', 'zhu2020deep': 'Deep-learning CNN accurately stages disease severity on portable chest x-ray of COVID-19 lung infection and may prove useful to stage lung disease severity, prognosticate, and predict treatment response and survival, thereby informing risk management and resource allocation.', 'hiraiwa2019a': 'The deep learning system showed high accuracy in the differential diagnosis of a single or extra root in the distal roots of mandibular first molars.', 'lemley2017deep': 'The current state of deep learning is reviewed, why it has managed to improve on the long-standing techniques of conventional neural networks is explained, and how to get started with adopting deep learning into your own research activities to solve both new and old problems and build better, smarter consumer devices and services.', 'joonmyun2020application': 'This paper surveys application trends in deep learning-based AI techniques for autonomous things, especially autonomous driving vehicles, because they present a wide range of problems involving perception, decision, and actions that are very common in other autonomous things.', 'sung2018facilitating': 'The experimental results showed that the students learning with the 3D experiential gaming system showed better learning achievements, problem-solving tendency, deep learning strategies, and deep learning motive than those who learned with the conventional technology-enhanced learning approach.', 'sridhar2020eeg': 'A deep learning network based on a sensory motor paradigm that employs a subject-agnostic Bidirectional Long Short-Term Memory (BLSTM) Network is developed to assess cognitive functions and identify its relationship with brain signal features, which is hypothesized to consistently indicate cognitive decline.', 'haensch2019the': 'A detailed analysis and design guidelines how nonvolatile memory materials need to be reengineered for optimal performance in the deep learning space shows a strong deviation from the materials used in memory applications.', 'zheng2022the': 'Artificial intelligence (AI) and reinforcement learning (RL) have improved many areas but are not yet widely adopted in economic policy design, mechanism design, or economics at large. The AI Economist is a two-level, deep RL framework for policy design in which agents and a social planner coadapt. In particular, the AI Economist uses structured curriculum learning to stabilize the challenging two-level, coadaptive learning problem. We validate this framework in the domain of taxation. In one-step economies, the AI Economist recovers the optimal tax policy of economic theory. In spatiotemporal economies, the AI Economist substantially improves both utilitarian social welfare and the trade-off between equality and productivity over baselines. It does so despite emergent tax-gaming strategies while accounting for emergent labor specialization, agent interactions, and behavioral change. These results demonstrate that two-level, deep RL complements economic theory and unlocks an AI-based approach to designing and understanding economic policy.', 'gee2019designing': 'There is today a great deal of controversy over digital and social media. Even leaders in the tech industry are beginning to decry the time young people spend on smartphones and social networks. Recently, the World Health Organization proposed adding ��gaming disorder�� to its official list of diseases, defining it as a pattern of gaming behavior so severe that it takes ��precedence over other life interests��. At the same time, many others have celebrated the positive properties of video games, social media, and social networks. This paper argues that a deeper understanding of human beings is needed to design for deep learning. For the purposes of this study ��design for deep learning�� means helping people matter and find meaning in ways that make them and others healthy in mind and body, while improving the state of the world for all living things, with due respect for truth, sensation, happiness, imagination, individuality, diversity, and the future. In particular, fifteen features related to human nature are suggested based on recent scientific developments to answer the question: What is a human being? Consequently, proposals that are linked to learning and transformation, as well as social improvement, should fit with the ways in which humans, as specific sorts of biological and social creatures, learn best (or can learn at all) and can change for the better. En la actualidad existe una nutrida controversia en relaci��n a los medios de comunicaci��n sociales y digitales que ha llevado, incluso, a censurar la utilizaci��n de las redes sociales y los m��viles por parte de l��deres en la industria tecnol��gica. En este sentido, la Organizaci��n Mundial para la Salud ha propuesto a\xf1adir el \xabdesorden del juego\xbb a su listado de enfermedades, defini��ndolo como un modelo de comportamiento de juego tan severo que se impone como \xabpreferencia sobre otros intereses\xbb. Al mismo tiempo, distintos acad��micos han enfatizado los aspectos positivos derivados de las redes sociales y los videojuegos. En este art��culo se argumenta que es necesaria una mejor comprensi��n del ser humano para poder implementar lo que aqu�� se define como dise\xf1o para el aprendizaje profundo. El \xabdise\xf1o para el aprendizaje profundo\xbb est�� encaminado al reconocimiento de las personas y el desarrollo de sentidos saludables, individual y colectivamente, as�� como la mejora, en general, del estado del mundo para todos los seres vivos, seg��n principios de verdad, felicidad, imaginaci��n, individualidad, diversidad y futuro. En particular, se sugieren quince caracter��sticas basadas en desarrollos cient��ficos que responden a la pregunta: \xbfQu�� es un ser humano? Consecuentemente, propuestas vinculadas al aprendizaje y la transformaci��n y mejora social deben ser coherentes con dichas caracter��sticas que permiten definir c��mo las personas, en tanto que organismos biol��gicos y sociales, aprenden o pueden aprender ��ptimamente, as�� como cambiar para mejorar.'}Your response should start with \section{related works}. Ensure that it can be directly compiled by LeTaX. | |
| INFO:utils.gpt_interaction:\section{Related Works} | |
| This section presents a review of related works in the field of deep reinforcement learning, with a focus on the application of this technique to playing Atari games. We categorize the related works into five main fields: (1) deep learning for game playing, (2) deep reinforcement learning, (3) actor-critic algorithms, (4) deep Q-networks, and (5) applications of deep reinforcement learning. | |
| \paragraph{Deep Learning for Game Playing} | |
| The use of deep learning for game playing has seen significant progress in recent years. The seminal work of \citet{mnih2013playing} introduced a deep reinforcement learning approach to playing Atari games, which achieved superhuman performance on several games. \citet{qi2016pointnet} proposed a neural network that directly consumes point clouds, which provides a unified architecture for applications ranging from object classification to scene semantic parsing. \citet{chollet2016xception} proposed a novel deep convolutional neural network architecture inspired by Inception, which slightly outperforms Inception V3 on the ImageNet dataset. \citet{zhang2016understanding} established that state-of-the-art convolutional networks for image classification trained with stochastic gradient methods easily fit a random labeling of the training data. \citet{gulshan2016development} developed an algorithm based on deep machine learning that had high sensitivity and specificity for detecting referable diabetic retinopathy and diabetic macular edema in retinal fundus photographs from adults with diabetes. | |
| \paragraph{Deep Reinforcement Learning} | |
| Deep reinforcement learning is a subfield of machine learning that combines deep learning and reinforcement learning. \citet{mnih2013playing} introduced the concept of deep reinforcement learning to playing Atari games. \citet{mnih2016asynchronous} proposed a lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers. \citet{haarnoja2018soft} proposed soft actor-critic, an off-policy actor-critic deep RL algorithm based on the maximum entropy reinforcement learning framework. \citet{rakkini2022comprehensive} surveyed the deployment of machine learning and deep learning, reinforcement methods on mitigating the selfish mining attacks in the blockchain. | |
| \paragraph{Actor-Critic Algorithms} | |
| Actor-critic algorithms are a class of reinforcement learning algorithms that use separate networks to estimate the value function and the policy. \citet{lillicrap2015continuous} presented an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. \citet{hasselt2015deep} proposed a specific adaptation to the DQN algorithm that reduces the observed overestimations and leads to much better performance on several games. \citet{xiong2018parametrized} proposed a parametrized deep Q-network (P-DQN) framework for the hybrid action space without approximation or relaxation, and combines the spirits of both DQN and DDPG by seamlessly integrating them. | |
| \paragraph{Deep Q-Networks} | |
| Deep Q-networks (DQNs) are a class of deep reinforcement learning algorithms that use a neural network to approximate the Q-value function. \citet{mnih2013playing} presented the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. \citet{haensch2019the} analyzed and designed guidelines for how nonvolatile memory materials need to be reengineered for optimal performance in the deep learning space. \citet{zhu2020deep} showed that deep-learning CNN accurately stages disease severity on portable chest x-ray of COVID-19 lung infection. | |
| \paragraph{Applications of Deep Reinforcement Learning} | |
| Deep reinforcement learning has been applied to various fields beyond game playing. \citet{sridhar2020eeg} developed a deep learning network based on a sensory motor paradigm that employs a subject-agnostic Bidirectional Long Short-Term Memory (BLSTM) Network to assess cognitive functions. \citet{joonmyun2020application} surveyed application trends in deep learning-based AI techniques for autonomous things. \citet{sung2018facilitating} showed that students learning with the 3D experiential gaming system showed better learning achievements, problem-solving tendency, deep learning strategies, and deep learning motive than those who learned with the conventional technology-enh | |
| INFO:root:For generating related works, 4097 tokens have been used (3194 for prompts; 903 for completion). 8279 tokens have been used in total. | |
| INFO:utils.gpt_interaction:{"Cloud Computing": 9, "Edge Computing": 9, "Multi-Agent Systems": 8, "Cooperative Game Theory": 7, "Collaborative Computing": 6} | |
| INFO:root:For generating keywords, 189 tokens have been used (149 for prompts; 40 for completion). 8468 tokens have been used in total. | |
| INFO:utils.prompts:Generated prompts for introduction: Your task is to write the introduction section of the paper with the title 'Research on Cloud Edge Collaborative Multi star Cooperative Game Strategy'. | |
| Your response should follow the following instructions: | |
| - Include five paragraph: Establishing the motivation for the research. Explaining its importance and relevance to the AI community. Clearly state the problem you're addressing, your proposed solution, and the specific research questions or objectives. Briefly mention key related works for context and explain the main differences from this work. List three novel contributions of this paper. | |
| - Start with \section{introduction} | |
| - Read references. Every time you use information from the references, you need to appropriately cite it (using \citep or \citet).For example of \citep, the sentence where you use information from lei2022adaptive \citep{lei2022adaptive}. For example of \citet, \citet{lei2022adaptive} claims some information. | |
| - Avoid citing the same reference in a same paragraph. | |
| References: | |
| {'wang2022hybrid': 'This article aims to systematically address the leader-following consensus issue for continuous-time multiagent systems with semi-Markov jump parameters. A more universal random process, semi-Markov process, is utilized to model the variations of parameters which may result from the complexity of environment. The innovative hybrid event-triggered strategy associated with an improved threshold function is employed for economizing the network bandwidth resources. It presents an exponential decay term in the threshold function for decreasing the transmission frequency of superfluous data packets, ulteriorly reducing triggering times. The dominating core concentrates on the design of a controller interrelated with the hybrid event-triggered condition, which can be implemented to ensure the leader-following consensus of the considered systems. On the strength of time-dependent and piecewise Lyapunov�CKrasovskii functional, several criteria, which are capable of ensuring the leader-following consensus of the multiagent systems, are derived. Ultimately, two suitable numerical examples and a meaningful practical one are utilized to demonstrate the validity and potential of the derived results.', 'li2016consensus': 'A distributed observer-type consensus protocol based on relative output measurements is proposed and a new framework is introduced to address in a unified way the consensus of multiagent systems and the synchronization of complex networks.', 'liang2021containment': 'Two kinds of classical control schemes are utilized to address the proposed synthesis problem of the containment control with respect to continuous-time semi- Markovian multiagent systems with semi-Markovian switching topologies.', 'li2021adaptive': 'A new lemma about the relative threshold event-triggered control strategy is proposed to handle the compensation error, which can improve the utilization of communication resources and weaken the negative impact on tracking accuracy and closed-loop system stability.', 'ma2021an': 'This book takes a close look at recent progress in the field of supply chain management using agent technology and more specifically multiagent systems. Sixteen chapters are organized in four main parts: Introductory Papers; Multiagent Based Supply Chain Modeling; Collaboration and Coordination Between Agents in a Supply Chain; and Multiagent Based Supply Chain Management: Applications. The result is a comprehensive review of existing literature, and ideas for future research. PAAMS, the International Conference on Practical Applications of Agents and Multi-Agent Systems is an evolution of the International Workshop on Practical Applications of Agents and Multi-Agent Systems. PAAMS is an international yearly tribune to present, to discuss, and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to exchange their experience in the development of Agents and Multi-Agent Systems. This volume presents the papers that have been accepted for the 2011 in the workshops: Workshop on Agents for Ambient Assisted Living, Workshop on Agent-Based', 'ni2021predefined': 'It is mathematically proved that the followers�� states can track the leader��s trajectory within predefined time and the settling time bound is directly related to tunable parameters, which facilitates the control protocol design to meet the desired convergence time requirement.', 'xu2021distributed': 'It is proved that under two controllers, the SP not only can be achieved in a fully distributed way without continuous communication for both the controller updates and the triggering condition detecting but also exclusion of ��Zeno behavior�� can be realized.', 'cui2021sampled': 'In this paper, the sampled-based consensus problem is investigated for a class of nonlinear multiagent systems subjected to deception attacks and the eigenvector of Laplacian matrix is utilized to construct a novel Lyapunov functional.', 'zhou2021event': 'An adaptive fuzzy event-triggered containment control scheme is proposed, which is conditionally updated only at the sampled instants and it is proved that all signals in the closed-loop system are semiglobally uniformly ultimately bounded.', 'zhang2022deadline': 'A dynamic time-sensitive scheduling algorithm with greedy strategy (TSGS) that ranks server capability and job size in a hybrid and hierarchical scenario is proposed and is proposed to employ comprehensive execution capability (CEC) to measure the performance of a server for the first time and perform effective server load balancing while satisfying the user��s requirement for tasks.', 'yang2022security': 'The principle, classification, and threat model of FL in edge�\cloud collaboration, which helps understand the challenges faced by edge�\ cloud collaborative computing, is introduced.', 'jiang2021a': 'A thing-edge-cloud collaborative computing decision-making (TCCD) method in customized production is proposed and the effectiveness of the proposed TCCD method is verified by using the prototype platform of personalized product packaging intelligent production line with the same process flow.', 'rong2021an': 'This paper presents an industrial edge-cloud collaborative computing platform, namely Sophon Edge, that helps to build and deploy AIoT applications efficiently and supports an iterative way for model evolution and updating so as to enable the AIeT applications agile and data-driven.', 'leng2021collaborative': 'The joint task offloading and scheduling is formulated as a Markov decision process with optimal communication and computing resource allocation, and deep reinforcement learning is utilized to obtain long-term benefits.', 'ren2020edge': 'This article proposes the first edge-based collaborative object recognition solution for mobile Web AR in the 5G era with finegrained and adaptive DNN partitioning for the collaboration between the cloud, the edge, and the mobile Web browser and proposes a differentiated DNN computation scheduling approach specially designed for the edge platform.', 'gao2020q': 'A novel Q-learning based computation offloading (QLCOF) policy to achieve the optimal resource allocation and offloading scheme by prescheduling the computation side for each task from a global perspective is designed.', 'wang2020enabling': 'This work considers the collaborative computing system where a user offloads the computation-intensive and latency-sensitive tasks to multiple idle computing devices by a centralized computing sharing platform (CSP) and proposes a computational latency-based pricing mechanism from the perspective of the quality-of-experience performance.', 'li2020collaborative': 'Simulation results show that the collaborative computing approach can adapt to different service environments and outperform the greedy offloading approach.', 'fadlullah2022hcp': 'A 2-stage federated learning algorithm among the UEs, UAVs/BSs, and HCP to collaboratively predict the content caching placement by jointly considering traffic distribution, UE mobility and localized content popularity is proposed.', 'yang2022output': 'In theory, sufficient conditions for stability are presented and analyzed, and it is proved that the proposed cloud-based control scheme renders the MAS stable and to achieve output consensus.', 'tan2021distributed': 'A novel networked model-free adaptive predictive control (NMFAPC) strategy combining networked predictive control theory with model- free adaptive control method is proposed, which provides a solution for the coordination of large-scale nonlinear MASs.', 'liu2020coordinated': 'A distributed cloud predictive control scheme is proposed to achieve desired coordination control performance and compensate actively for communication delays between the cloud computing nodes and between the agents.', 'liu2017predictive': 'A cloud predictive control scheme for networked multiagent systems (NMASs) is proposed to achieve consensus and stability simultaneously and to compensate for network delays actively to provide a foundation for the development of cooperative and coordinative control of NMASs and its applications.', 'liu2022coordinated': 'A mist�Cfog�Ccloud predictive control scheme is proposed for the coordinated control of complex large-scale networked multiagent systems by making use of the advantages of cloud edge computing.', 'yang2021predictive': 'A predictive cloud control scheme is investigated for a networked multiagent system (NMAS) with quantized signals under denial-of-service (DoS) attacks with effectiveness and computing superiority demonstrated by simulation results.', 'ageed2020unified': 'Using ontology enhances the cloud computing selfmotivated via an intelligent framework of SaaS and consolidating the security by providing resources access control and RDF and OWL semantic technologies in the modeling of a multiagent system are very effective in increases coordination the interoperability.', 'tan2020data': "To actively compensate for network delays and data losses in all communication channels and coordinate the output of the CBMF-MAS, a novel data-driven networked distributed predictive control method (DDNDPC) is presented, which is independent of system's structure model and only relies on system��s input and output data.", 'erg��n2020an': 'This paper mathematically associates Crypto Cloud Computing with Cooperative Game Theory in the presence of uncertainty, and suggests some interval solutions related with the model by proposing a novel elliptic curve public key encryption scheme over finite fields having the property of semantic security.', 'prasadu2018implementing': 'A non-cooperative game based task scheduling and computing resource allocation algorithm NG_TSRA is put forward that can improve the average power efficiency of the cloud computing system and proves the existence of Nash Equilibrium point of the game.', 'wang2021multi': 'Simulation results validate that the TPOA and GT-MSTO can effectively obtain the minimum task delay, and introduce a multi-server cooperative offloading strategy for IoT systems.', 'khandelwal2019a': 'This thesis forms the optimal coalition formation in federated clouds as an integer linear programming problem under the cloud service brokerage model proposed by Mashayekhy et al and proposes a fast polynomial time greedy algorithm to find a near optimal coalition.', 'kim2021a': 'This study designs a novel DMCC resource sharing scheme for data offloading services that explores the mutual benefits of local and global MCC providers�� interactions, and effectively shares the distributed cloud resources.', 'ananth2015cooperative': 'New job scheduling technique is proposed using the concepts of game theory and genetic algorithm to provide Pareto optimal solution using Non-dominated Sorting Genetic Algorithm II (NSGA II) and also concentrates on minimizing the deadline violation and makespan for the jobs submitted by the user.', 'bataineh2021cloud': 'It is argued in this paper that the role of the cloud should be reshaped from being a passive virtual market to become an active platform for monetizing data and a novel game theoretical model is proposed, which consists of a mix of cooperative and competitive strategies.', 'houda2022when': 'This article designs a novel MEC-based framework to secure IIoT applications leveraging FL, called FedGame, and evaluates its accuracy against centralized ML/DL schemes while preserving the privacy of Industrial systems but also its efficiency in providing required MECs resources and, thus, dealing withIIoT attacks.', 'moura2019game': 'This chapter discusses relevant theoretical models that enable cooperation amongst the players in distinct ways through, namely, pricing or reputation and highlights open problems, such as the lack of proper models for dynamic and incomplete information scenarios.'}Your response should start with \section{introduction}. Ensure that it can be directly compiled by LeTaX. | |
| INFO:utils.gpt_interaction:\section{Introduction} | |
| The emergence of edge computing has brought about new opportunities for collaborative computing. Cloud edge collaborative computing has been widely used in many fields, such as industrial automation, smart cities, and the Internet of Things (IoT). In this context, multiagent systems (MASs) have been proposed as a promising approach for distributed and collaborative computing. However, the traditional MASs still face challenges such as communication delays, limited bandwidth, and security issues. To address these challenges, we propose a cloud edge collaborative multi-star cooperative game strategy for MASs. | |
| The proposed strategy aims to improve the efficiency and security of cloud edge collaborative computing by introducing a cooperative game model. The model considers multiple stars, where each star represents a group of agents that collaborate with each other to achieve a common goal. The game model is designed to encourage cooperation among the stars and ensure the stability of the system. The proposed strategy also incorporates edge computing to reduce communication delays and improve the efficiency of the system. | |
| The importance and relevance of this research lie in the fact that it addresses the challenges of cloud edge collaborative computing and proposes a novel solution that can improve the efficiency and security of the system. The proposed strategy can be applied in various fields, such as industrial automation, smart cities, and the IoT, where cloud edge collaborative computing is widely used. | |
| The specific research questions that this paper aims to address include: How can we improve the efficiency and security of cloud edge collaborative computing? How can we encourage cooperation among multiple stars in a MAS? How can we ensure the stability of the system? To answer these questions, we propose a cloud edge collaborative multi-star cooperative game strategy for MASs. | |
| Related works have been proposed in the literature, such as the work of \citet{li2021adaptive} and \citet{yang2022security}. However, the proposed strategy differs from these works in that it introduces a cooperative game model that considers multiple stars and incorporates edge computing to improve the efficiency of the system. Additionally, this paper proposes three novel contributions: (1) the introduction of a cooperative game model for cloud edge collaborative computing, (2) the consideration of multiple stars in the game model, and (3) the incorporation of edge computing to improve the efficiency of the system. | |
| INFO:root:For generating introduction, 2983 tokens have been used (2528 for prompts; 455 for completion). 11451 tokens have been used in total. | |
| INFO:utils.prompts:Generated prompts for related works: Your task is to write the related works section of the paper with the title 'Research on Cloud Edge Collaborative Multi star Cooperative Game Strategy'. | |
| Your response should follow the following instructions: | |
| - Discuss three to five main related fields to this paper. For each field, select five to ten key publications from references. For each reference, analyze its strengths and weaknesses in one or two sentences. Present the related works in a logical manner, often chronologically. Consider using a taxonomy or categorization to structure the discussion. Do not use \section{...} or \subsection{...}; use \paragraph{...} to list related fields. | |
| - Start with \section{related works} | |
| - Read references. Every time you use information from the references, you need to appropriately cite it (using \citep or \citet).For example of \citep, the sentence where you use information from lei2022adaptive \citep{lei2022adaptive}. For example of \citet, \citet{lei2022adaptive} claims some information. | |
| - Avoid citing the same reference in a same paragraph. | |
| References: | |
| {'wang2022hybrid': 'This article aims to systematically address the leader-following consensus issue for continuous-time multiagent systems with semi-Markov jump parameters. A more universal random process, semi-Markov process, is utilized to model the variations of parameters which may result from the complexity of environment. The innovative hybrid event-triggered strategy associated with an improved threshold function is employed for economizing the network bandwidth resources. It presents an exponential decay term in the threshold function for decreasing the transmission frequency of superfluous data packets, ulteriorly reducing triggering times. The dominating core concentrates on the design of a controller interrelated with the hybrid event-triggered condition, which can be implemented to ensure the leader-following consensus of the considered systems. On the strength of time-dependent and piecewise Lyapunov�CKrasovskii functional, several criteria, which are capable of ensuring the leader-following consensus of the multiagent systems, are derived. Ultimately, two suitable numerical examples and a meaningful practical one are utilized to demonstrate the validity and potential of the derived results.', 'li2016consensus': 'A distributed observer-type consensus protocol based on relative output measurements is proposed and a new framework is introduced to address in a unified way the consensus of multiagent systems and the synchronization of complex networks.', 'liang2021containment': 'Two kinds of classical control schemes are utilized to address the proposed synthesis problem of the containment control with respect to continuous-time semi- Markovian multiagent systems with semi-Markovian switching topologies.', 'li2021adaptive': 'A new lemma about the relative threshold event-triggered control strategy is proposed to handle the compensation error, which can improve the utilization of communication resources and weaken the negative impact on tracking accuracy and closed-loop system stability.', 'ma2021an': 'This book takes a close look at recent progress in the field of supply chain management using agent technology and more specifically multiagent systems. Sixteen chapters are organized in four main parts: Introductory Papers; Multiagent Based Supply Chain Modeling; Collaboration and Coordination Between Agents in a Supply Chain; and Multiagent Based Supply Chain Management: Applications. The result is a comprehensive review of existing literature, and ideas for future research. PAAMS, the International Conference on Practical Applications of Agents and Multi-Agent Systems is an evolution of the International Workshop on Practical Applications of Agents and Multi-Agent Systems. PAAMS is an international yearly tribune to present, to discuss, and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to exchange their experience in the development of Agents and Multi-Agent Systems. This volume presents the papers that have been accepted for the 2011 in the workshops: Workshop on Agents for Ambient Assisted Living, Workshop on Agent-Based', 'ni2021predefined': 'It is mathematically proved that the followers�� states can track the leader��s trajectory within predefined time and the settling time bound is directly related to tunable parameters, which facilitates the control protocol design to meet the desired convergence time requirement.', 'xu2021distributed': 'It is proved that under two controllers, the SP not only can be achieved in a fully distributed way without continuous communication for both the controller updates and the triggering condition detecting but also exclusion of ��Zeno behavior�� can be realized.', 'cui2021sampled': 'In this paper, the sampled-based consensus problem is investigated for a class of nonlinear multiagent systems subjected to deception attacks and the eigenvector of Laplacian matrix is utilized to construct a novel Lyapunov functional.', 'zhou2021event': 'An adaptive fuzzy event-triggered containment control scheme is proposed, which is conditionally updated only at the sampled instants and it is proved that all signals in the closed-loop system are semiglobally uniformly ultimately bounded.', 'zhang2022deadline': 'A dynamic time-sensitive scheduling algorithm with greedy strategy (TSGS) that ranks server capability and job size in a hybrid and hierarchical scenario is proposed and is proposed to employ comprehensive execution capability (CEC) to measure the performance of a server for the first time and perform effective server load balancing while satisfying the user��s requirement for tasks.', 'yang2022security': 'The principle, classification, and threat model of FL in edge�\cloud collaboration, which helps understand the challenges faced by edge�\ cloud collaborative computing, is introduced.', 'jiang2021a': 'A thing-edge-cloud collaborative computing decision-making (TCCD) method in customized production is proposed and the effectiveness of the proposed TCCD method is verified by using the prototype platform of personalized product packaging intelligent production line with the same process flow.', 'rong2021an': 'This paper presents an industrial edge-cloud collaborative computing platform, namely Sophon Edge, that helps to build and deploy AIoT applications efficiently and supports an iterative way for model evolution and updating so as to enable the AIeT applications agile and data-driven.', 'leng2021collaborative': 'The joint task offloading and scheduling is formulated as a Markov decision process with optimal communication and computing resource allocation, and deep reinforcement learning is utilized to obtain long-term benefits.', 'ren2020edge': 'This article proposes the first edge-based collaborative object recognition solution for mobile Web AR in the 5G era with finegrained and adaptive DNN partitioning for the collaboration between the cloud, the edge, and the mobile Web browser and proposes a differentiated DNN computation scheduling approach specially designed for the edge platform.', 'gao2020q': 'A novel Q-learning based computation offloading (QLCOF) policy to achieve the optimal resource allocation and offloading scheme by prescheduling the computation side for each task from a global perspective is designed.', 'wang2020enabling': 'This work considers the collaborative computing system where a user offloads the computation-intensive and latency-sensitive tasks to multiple idle computing devices by a centralized computing sharing platform (CSP) and proposes a computational latency-based pricing mechanism from the perspective of the quality-of-experience performance.', 'li2020collaborative': 'Simulation results show that the collaborative computing approach can adapt to different service environments and outperform the greedy offloading approach.', 'fadlullah2022hcp': 'A 2-stage federated learning algorithm among the UEs, UAVs/BSs, and HCP to collaboratively predict the content caching placement by jointly considering traffic distribution, UE mobility and localized content popularity is proposed.', 'yang2022output': 'In theory, sufficient conditions for stability are presented and analyzed, and it is proved that the proposed cloud-based control scheme renders the MAS stable and to achieve output consensus.', 'tan2021distributed': 'A novel networked model-free adaptive predictive control (NMFAPC) strategy combining networked predictive control theory with model- free adaptive control method is proposed, which provides a solution for the coordination of large-scale nonlinear MASs.', 'liu2020coordinated': 'A distributed cloud predictive control scheme is proposed to achieve desired coordination control performance and compensate actively for communication delays between the cloud computing nodes and between the agents.', 'liu2017predictive': 'A cloud predictive control scheme for networked multiagent systems (NMASs) is proposed to achieve consensus and stability simultaneously and to compensate for network delays actively to provide a foundation for the development of cooperative and coordinative control of NMASs and its applications.', 'liu2022coordinated': 'A mist�Cfog�Ccloud predictive control scheme is proposed for the coordinated control of complex large-scale networked multiagent systems by making use of the advantages of cloud edge computing.', 'yang2021predictive': 'A predictive cloud control scheme is investigated for a networked multiagent system (NMAS) with quantized signals under denial-of-service (DoS) attacks with effectiveness and computing superiority demonstrated by simulation results.', 'ageed2020unified': 'Using ontology enhances the cloud computing selfmotivated via an intelligent framework of SaaS and consolidating the security by providing resources access control and RDF and OWL semantic technologies in the modeling of a multiagent system are very effective in increases coordination the interoperability.', 'tan2020data': "To actively compensate for network delays and data losses in all communication channels and coordinate the output of the CBMF-MAS, a novel data-driven networked distributed predictive control method (DDNDPC) is presented, which is independent of system's structure model and only relies on system��s input and output data.", 'erg��n2020an': 'This paper mathematically associates Crypto Cloud Computing with Cooperative Game Theory in the presence of uncertainty, and suggests some interval solutions related with the model by proposing a novel elliptic curve public key encryption scheme over finite fields having the property of semantic security.', 'prasadu2018implementing': 'A non-cooperative game based task scheduling and computing resource allocation algorithm NG_TSRA is put forward that can improve the average power efficiency of the cloud computing system and proves the existence of Nash Equilibrium point of the game.', 'wang2021multi': 'Simulation results validate that the TPOA and GT-MSTO can effectively obtain the minimum task delay, and introduce a multi-server cooperative offloading strategy for IoT systems.', 'khandelwal2019a': 'This thesis forms the optimal coalition formation in federated clouds as an integer linear programming problem under the cloud service brokerage model proposed by Mashayekhy et al and proposes a fast polynomial time greedy algorithm to find a near optimal coalition.', 'kim2021a': 'This study designs a novel DMCC resource sharing scheme for data offloading services that explores the mutual benefits of local and global MCC providers�� interactions, and effectively shares the distributed cloud resources.', 'ananth2015cooperative': 'New job scheduling technique is proposed using the concepts of game theory and genetic algorithm to provide Pareto optimal solution using Non-dominated Sorting Genetic Algorithm II (NSGA II) and also concentrates on minimizing the deadline violation and makespan for the jobs submitted by the user.', 'bataineh2021cloud': 'It is argued in this paper that the role of the cloud should be reshaped from being a passive virtual market to become an active platform for monetizing data and a novel game theoretical model is proposed, which consists of a mix of cooperative and competitive strategies.', 'houda2022when': 'This article designs a novel MEC-based framework to secure IIoT applications leveraging FL, called FedGame, and evaluates its accuracy against centralized ML/DL schemes while preserving the privacy of Industrial systems but also its efficiency in providing required MECs resources and, thus, dealing withIIoT attacks.', 'moura2019game': 'This chapter discusses relevant theoretical models that enable cooperation amongst the players in distinct ways through, namely, pricing or reputation and highlights open problems, such as the lack of proper models for dynamic and incomplete information scenarios.'}Your response should start with \section{related works}. Ensure that it can be directly compiled by LeTaX. | |
| INFO:utils.gpt_interaction:\section{Related Works} | |
| In this section, we review the related works in the field of cloud edge collaborative multi-star cooperative game strategy. We categorize the related works into several fields and analyze their strengths and weaknesses. | |
| \paragraph{Consensus and Control in Multi-Agent Systems} | |
| Several works have addressed the consensus and control issues in multi-agent systems. For instance, Li et al. \citep{li2016consensus} proposed a distributed observer-type consensus protocol based on relative output measurements. Wang et al. \citep{wang2022hybrid} addressed the leader-following consensus issue for continuous-time multi-agent systems with semi-Markov jump parameters. Liang et al. \citep{liang2021containment} utilized two kinds of classical control schemes to address the proposed synthesis problem of the containment control with respect to continuous-time semi-Markovian multi-agent systems with semi-Markovian switching topologies. These works provide theoretical foundations for the consensus and control in multi-agent systems. | |
| \paragraph{Collaborative Computing in Edge-Cloud Systems} | |
| Collaborative computing in edge-cloud systems has been extensively studied in recent years. Liu et al. \citep{liu2020coordinated} proposed a distributed cloud predictive control scheme to achieve desired coordination control performance and compensate actively for communication delays between the cloud computing nodes and between the agents. Liu et al. \citep{liu2017predictive} proposed a cloud predictive control scheme for networked multi-agent systems (NMASs) to achieve consensus and stability simultaneously and to compensate for network delays actively. Liu et al. \citep{liu2022coordinated} proposed a mist-fog-cloud predictive control scheme for the coordinated control of complex large-scale networked multi-agent systems. These works provide insights into the design of collaborative computing systems in edge-cloud environments. | |
| \paragraph{Game Theory and Cooperative Game Strategy} | |
| Game theory and cooperative game strategy have been widely used in the design of collaborative computing systems. Prasadu et al. \citep{prasadu2018implementing} proposed a non-cooperative game-based task scheduling and computing resource allocation algorithm NG_TSRA that can improve the average power efficiency of the cloud computing system. Moura et al. \citep{moura2019game} discussed relevant theoretical models that enable cooperation amongst the players in distinct ways through pricing or reputation and highlighted open problems. Bataineh et al. \citep{bataineh2021cloud} proposed a novel game theoretical model that consists of a mix of cooperative and competitive strategies. These works provide a theoretical basis for the design of game-theoretic models in collaborative computing systems. | |
| \paragraph{Federated Learning and Multi-Agent Systems} | |
| Federated learning and multi-agent systems have been combined to address various problems in cloud-edge collaborative computing. Fadlullah et al. \citep{fadlullah2022hcp} proposed a 2-stage federated learning algorithm among the UEs, UAVs/BSs, and HCP to collaboratively predict the content caching placement. Yang et al. \citep{yang2022output} presented a cloud-based control scheme for multi-agent systems and proved that the proposed scheme renders the MAS stable and achieves output consensus. Houda et al. \citep{houda2022when} designed a novel MEC-based framework to secure IIoT applications leveraging FL, called FedGame, and evaluated its accuracy against centralized ML/DL schemes while preserving the privacy of Industrial systems. These works provide insights into the integration of federated learning and multi-agent systems in cloud-edge collaborative computing. | |
| \paragraph{IoT Systems and Multi-Server Offloading Strategy} | |
| IoT systems and multi-server offloading strategy have been studied to optimize the performance of cloud-edge collaborative computing. Wang et al. \citep{wang2021multi} introduced a multi-server cooperative offloading strategy for IoT systems. Ananth et al. \citep{ananth2015cooperative} proposed a new job scheduling technique using the concepts of game theory and genetic algorithm to provide Pareto optimal solution using Non-dominated Sorting Genetic Algorithm II (NSGA II) and also concentrated on minimizing the deadline violation and makespan for the jobs submitted by the user. Kim et al. \citep{kim2021a} designed a novel DMCC resource sharing scheme for data offloading services that explores the mutual benefits of local and global MCC providers�� interactions. These works provide insights into the design of IoT systems and multi-server offloading strategy in cloud-edge collaborative computing. | |
| In summary, the related works in the field of cloud edge collaborative multi-star cooperative game strategy have been categorized into several fields, including consensus and control in multi-agent systems, collaborative computing in edge-cloud systems, game theory and cooperative game strategy, federated learning and multi-agent systems, and IoT systems and multi-server offloading strategy. These works provide theoretical foundations and practical insights into the design of cloud-edge collaborative computing systems. | |
| INFO:root:For generating related works, 3571 tokens have been used (2551 for prompts; 1020 for completion). 15022 tokens have been used in total. | |