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| centralized training decentralized execution: Centralized Control and Decentralized Execution: a Catchphrase in Crisis? Clint Hinote, 2012-07-23 The Air Force's master tenet of centralized control, decentralized execution is in danger of becoming dogma. Airmen have difficulty communicating the meaning of this phrase in a joint setting. This is partially due to our limited understanding of its history and the imprecise meaning of the words involved. Furthermore, the irregular conflicts in Afghanistan and Iraq (and the ongoing service debates in the Pacific) have demonstrated the need for a deeper understanding of this master tenet to advocate effectively for airpower solutions. We must get this right, as it is critical to maximizing airpower's potential. Getting it right, however, requires moving beyond sound bites and bumper stickers. |
| centralized training decentralized execution: Deep Learning and Reinforcement Learning , 2023-11-15 Deep learning and reinforcement learning are some of the most important and exciting research fields today. With the emergence of new network structures and algorithms such as convolutional neural networks, recurrent neural networks, and self-attention models, these technologies have gained widespread attention and applications in fields such as natural language processing, medical image analysis, and Internet of Things (IoT) device recognition. This book, Deep Learning and Reinforcement Learning examines the latest research achievements of these technologies and provides a reference for researchers, engineers, students, and other interested readers. It helps readers understand the opportunities and challenges faced by deep learning and reinforcement learning and how to address them, thus improving the research and application capabilities of these technologies in related fields. |
| centralized training decentralized execution: Applications of Machine Learning in UAV Networks Hassan, Jahan, Alsamhi, Saeed, 2024-01-17 Applications of Machine Learning in UAV Networks presents a pioneering exploration into the symbiotic relationship between machine learning techniques and UAVs. In an age where UAVs are revolutionizing sectors as diverse as agriculture, environmental preservation, security, and disaster response, this meticulously crafted volume offers an analysis of the manifold ways machine learning drives advancements in UAV network efficiency and efficacy. This book navigates through an expansive array of domains, each demarcating a pivotal application of machine learning in UAV networks. From the precision realm of agriculture and its dynamic role in yield prediction to the ecological sensitivity of biodiversity monitoring and habitat restoration, the contours of each domain are vividly etched. These explorations are not limited to the terrestrial sphere; rather, they extend to the pivotal aerial missions of wildlife conservation, forest fire monitoring, and security enhancement, where UAVs adorned with machine learning algorithms wield an instrumental role. Scholars and practitioners from fields as diverse as machine learning, UAV technology, robotics, and IoT networks will find themselves immersed in a confluence of interdisciplinary expertise. The book's pages cater equally to professionals entrenched in agriculture, environmental studies, disaster management, and beyond. |
| centralized training decentralized execution: Advanced Computational and Communication Paradigms Samarjeet Borah, Tapan K. Gandhi, Vincenzo Piuri, 2023-09-20 This book presents high-quality, peer-reviewed papers from Fourth International Conference on Advanced Computational and Communication Paradigms (ICACCP 2023), organized by Department of Computer Science and Engineering (CSE), Sikkim Manipal Institute of Technology (SMIT), Sikkim, India, during February 16–18, 2023. ICACCP 2023 covers advanced computational paradigms and communications technique which provides failsafe and robust solutions to the emerging problems faced by mankind. Technologists, scientists, industry professionals, and research scholars from regional, national, and international levels are invited to present their original unpublished work in this conference. |
| centralized training decentralized execution: Computational and Experimental Simulations in Engineering Shaofan Li, 2023-11-30 This book gathers the latest advances, innovations, and applications in the field of computational engineering, as presented by leading international researchers and engineers at the 29th International Conference on Computational & Experimental Engineering and Sciences (ICCES), held in Shenzhen, China on May 26-29, 2023. ICCES covers all aspects of applied sciences and engineering: theoretical, analytical, computational, and experimental studies and solutions of problems in the physical, chemical, biological, mechanical, electrical, and mathematical sciences. As such, the book discusses highly diverse topics, including composites; bioengineering & biomechanics; geotechnical engineering; offshore & arctic engineering; multi-scale & multi-physics fluid engineering; structural integrity & longevity; materials design & simulation; and computer modeling methods in engineering. The contributions, which were selected by means of a rigorous international peer-review process, highlight numerous exciting ideas that will spur novel research directions and foster multidisciplinary collaborations. |
| centralized training decentralized execution: Reinforcement Learning for Sequential Decision and Optimal Control Shengbo Eben Li, 2023-04-05 Have you ever wondered how AlphaZero learns to defeat the top human Go players? Do you have any clues about how an autonomous driving system can gradually develop self-driving skills beyond normal drivers? What is the key that enables AlphaStar to make decisions in Starcraft, a notoriously difficult strategy game that has partial information and complex rules? The core mechanism underlying those recent technical breakthroughs is reinforcement learning (RL), a theory that can help an agent to develop the self-evolution ability through continuing environment interactions. In the past few years, the AI community has witnessed phenomenal success of reinforcement learning in various fields, including chess games, computer games and robotic control. RL is also considered to be a promising and powerful tool to create general artificial intelligence in the future. As an interdisciplinary field of trial-and-error learning and optimal control, RL resembles how humans reinforce their intelligence by interacting with the environment and provides a principled solution for sequential decision making and optimal control in large-scale and complex problems. Since RL contains a wide range of new concepts and theories, scholars may be plagued by a number of questions: What is the inherent mechanism of reinforcement learning? What is the internal connection between RL and optimal control? How has RL evolved in the past few decades, and what are the milestones? How do we choose and implement practical and effective RL algorithms for real-world scenarios? What are the key challenges that RL faces today, and how can we solve them? What is the current trend of RL research? You can find answers to all those questions in this book. The purpose of the book is to help researchers and practitioners take a comprehensive view of RL and understand the in-depth connection between RL and optimal control. The book includes not only systematic and thorough explanations of theoretical basics but also methodical guidance of practical algorithm implementations. The book intends to provide a comprehensive coverage of both classic theories and recent achievements, and the content is carefully and logically organized, including basic topics such as the main concepts and terminologies of RL, Markov decision process (MDP), Bellman’s optimality condition, Monte Carlo learning, temporal difference learning, stochastic dynamic programming, function approximation, policy gradient methods, approximate dynamic programming, and deep RL, as well as the latest advances in action and state constraints, safety guarantee, reference harmonization, robust RL, partially observable MDP, multiagent RL, inverse RL, offline RL, and so on. |
| centralized training decentralized execution: Deep Reinforcement Learning Aske Plaat, 2022-06-10 Deep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very difficult. In the game of Go, the program AlphaGo has even learned to outmatch three of the world’s leading players.Deep reinforcement learning takes its inspiration from the fields of biology and psychology. Biology has inspired the creation of artificial neural networks and deep learning, while psychology studies how animals and humans learn, and how subjects’ desired behavior can be reinforced with positive and negative stimuli. When we see how reinforcement learning teaches a simulated robot to walk, we are reminded of how children learn, through playful exploration. Techniques that are inspired by biology and psychology work amazingly well in computers: animal behavior and the structure of the brain as new blueprints for science and engineering. In fact, computers truly seem to possess aspects of human behavior; as such, this field goes to the heart of the dream of artificial intelligence. These research advances have not gone unnoticed by educators. Many universities have begun offering courses on the subject of deep reinforcement learning. The aim of this book is to provide an overview of the field, at the proper level of detail for a graduate course in artificial intelligence. It covers the complete field, from the basic algorithms of Deep Q-learning, to advanced topics such as multi-agent reinforcement learning and meta learning. |
| centralized training decentralized execution: Artificial Intelligence in Industry 4.0 Alexiei Dingli, Foaad Haddod, Christina Klüver, 2021-02-27 This book is intended to help management and other interested parties such as engineers, to understand the state of the art when it comes to the intersection between AI and Industry 4.0 and get them to realise the huge possibilities which can be unleashed by the intersection of these two fields. We have heard a lot about Industry 4.0, but most of the time, it focuses mainly on automation. In this book, the authors are going a step further by exploring advanced applications of Artificial Intelligence (AI) techniques, ranging from the use of deep learning algorithms in order to make predictions, up to an implementation of a full-blown Digital Triplet system. The scope of the book is to showcase what is currently brewing in the labs with the hope of migrating these technologies towards the factory floors. Chairpersons and CEOs must read these papers if they want to stay at the forefront of the game, ahead of their competition, while also saving huge sums of money in the process. |
| centralized training decentralized execution: Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology Jian Dong, |
| centralized training decentralized execution: Artificial Intelligence and Robotics Huimin Lu, Jintong Cai, 2024-01-03 This book constitutes the refereed proceedings of the 8th International Symposium on Artificial Intelligence and Robotics, ISAIR 2023, held in Beijing, China, during October 21–23, 2023. The 50 full papers included in this book were carefully reviewed and selected from 103 submissions. They focus on three important areas of Pattern Recognition: Artificial Intelligence; Robotics and Internet of Things, Covering Various Technical Aspects. |
| centralized training decentralized execution: Artificial Neural Networks and Machine Learning – ICANN 2022 Elias Pimenidis, Plamen Angelov, Chrisina Jayne, Antonios Papaleonidas, Mehmet Aydin, 2022-09-06 The 4-volumes set of LNCS 13529, 13530, 13531, and 13532 constitutes the proceedings of the 31st International Conference on Artificial Neural Networks, ICANN 2022, held in Bristol, UK, in September 2022. The total of 255 full papers presented in these proceedings was carefully reviewed and selected from 561 submissions. ICANN 2022 is a dual-track conference featuring tracks in brain inspired computing and machine learning and artificial neural networks, with strong cross-disciplinary interactions and applications. Chapters “Learning Flexible Translation Between Robot Actions and Language Descriptions”, “Learning Visually Grounded Human-Robot Dialog in a Hybrid Neural Architecture” are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com. |
| centralized training decentralized execution: Advances in Swarm Intelligence Ying Tan, |
| centralized training decentralized execution: Explainable Agency in Artificial Intelligence Silvia Tulli, David W. Aha, 2024-01-22 This book focuses on a subtopic of explainable AI (XAI) called explainable agency (EA), which involves producing records of decisions made during an agent’s reasoning, summarizing its behavior in human-accessible terms, and providing answers to questions about specific choices and the reasons for them. We distinguish explainable agency from interpretable machine learning (IML), another branch of XAI that focuses on providing insight (typically, for an ML expert) concerning a learned model and its decisions. In contrast, explainable agency typically involves a broader set of AI-enabled techniques, systems, and stakeholders (e.g., end users), where the explanations provided by EA agents are best evaluated in the context of human subject studies. The chapters of this book explore the concept of endowing intelligent agents with explainable agency, which is crucial for agents to be trusted by humans in critical domains such as finance, self-driving vehicles, and military operations. This book presents the work of researchers from a variety of perspectives and describes challenges, recent research results, lessons learned from applications, and recommendations for future research directions in EA. The historical perspectives of explainable agency and the importance of interactivity in explainable systems are also discussed. Ultimately, this book aims to contribute to the successful partnership between humans and AI systems. Features: Contributes to the topic of explainable artificial intelligence (XAI) Focuses on the XAI subtopic of explainable agency Includes an introductory chapter, a survey, and five other original contributions |
| centralized training decentralized execution: Deep Learning for Power System Applications Fangxing Li, Yan Du, 2023-12-12 This book provides readers with an in-depth review of deep learning-based techniques and discusses how they can benefit power system applications. Representative case studies of deep learning techniques in power systems are investigated and discussed, including convolutional neural networks (CNN) for power system security screening and cascading failure assessment, deep neural networks (DNN) for demand response management, and deep reinforcement learning (deep RL) for heating, ventilation, and air conditioning (HVAC) control. Deep Learning for Power System Applications: Case Studies Linking Artificial Intelligence and Power Systems is an ideal resource for professors, students, and industrial and government researchers in power systems, as well as practicing engineers and AI researchers. Provides a history of AI in power grid operation and planning; Introduces deep learning algorithms and applications in power systems; Includes several representative case studies. |
| centralized training decentralized execution: Service-Oriented Computing Flavia Monti, Stefanie Rinderle-Ma, Antonio Ruiz Cortés, Zibin Zheng, Massimo Mecella, 2023-11-21 These two volumes constitute the proceedings of the 21st International Conference, ICSOC 2023, held Rome, Italy, during November 28–December 1, 2023. The 35 full papers and the 10 short papers included in this volume were carefully reviewed and selected from 208 submissions. The volumes focus on cutting-edge topics like artificial intelligence, machine learning, big data analytics, the Internet of Things (IoT), and emerging technologies such as quantum computing, blockchain, chatbots, and sustainable green IT solutions. |
| centralized training decentralized execution: Research Report , 2007 |
| centralized training decentralized execution: Wireless Algorithms, Systems, and Applications Zhe Liu, Fan Wu, Sajal K. Das, 2021-09-08 The three-volume set constitutes the proceedings of the 16th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2021, which was held during June 25-27, 2021. The conference took place in Nanjing, China.The 103 full and 57 short papers presented in these proceedings were carefully reviewed and selected from 315 submissions. The contributions in Part II of the set are subdivided into the following topical sections: Scheduling & Optimization II; Security; Data Center Networks and Cloud Computing; Privacy-Aware Computing; Internet of Vehicles; Visual Computing for IoT; Mobile Ad-Hoc Networks. |
| centralized training decentralized execution: PRICAI 2023: Trends in Artificial Intelligence Fenrong Liu, Arun Anand Sadanandan, Duc Nghia Pham, Petrus Mursanto, Dickson Lukose, 2023-11-10 This three-volume set, LNCS 14325-14327 constitutes the thoroughly refereed proceedings of the 20th Pacific Rim Conference on Artificial Intelligence, PRICAI 2023, held in Jakarta, Indonesia, in November 2023. The 95 full papers and 36 short papers presented in these volumes were carefully reviewed and selected from 422 submissions. PRICAI covers a wide range of topics in the areas of social and economic importance for countries in the Pacific Rim: artificial intelligence, machine learning, natural language processing, knowledge representation and reasoning, planning and scheduling, computer vision, distributed artificial intelligence, search methodologies, etc. |
| centralized training decentralized execution: Emerging Cutting-Edge Developments in Intelligent Traffic and Transportation Systems M. Shafik, 2024-03-05 With the advent and development of AI and other new technologies, traffic and transportation have changed enormously in recent years, and the need for more environmentally-friendly solutions is also driving innovation in these fields. This book presents the proceedings of ICITT 2023, the 7th International Conference on Intelligent Traffic and Transportation, held from 18-20 September 2023 in Madrid, Spain. This annual conference is becoming one of the leading international conferences for presenting novel and fundamental advances in the fields of intelligent traffic and transportation. It also serves to foster communication among researchers and practitioners working in a wide variety of scientific areas with a common interest in intelligent traffic and transportation and related techniques. ICITT welcomes scholars and researchers from all over the world to share experiences and lessons with other enthusiasts, and develop opportunities for cooperation. The 27 papers included here represent an acceptance rate of 64% of submissions received, and were selected following a rigorous review process. Topics covered include autonomous technology; industrial automation; artificial intelligence; machine, deep and cognitive learning; distributed networking; transportation in future smart cities; hybrid vehicle technology; mobility; cyber-physical systems; design and cost engineering; enterprise information management; product design; intelligent automation; ICT-enabled collaborative global manufacturing; knowledge management; product-service systems; optimization; product lifecycle management; sustainable systems; machine vision; Industry 4.0; and navigation systems. Offering an overview of recent research and current practice, the book will be of interest to all those working in the field. |
| centralized training decentralized execution: Reinforcement Learning in the Ridesharing Marketplace Zhiwei (Tony) Qin, |
| centralized training decentralized execution: Distributional Reinforcement Learning Marc G. Bellemare, Will Dabney, Mark Rowland, 2023-05-30 The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective. Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. Going beyond the common approach to reinforcement learning and expected values, it focuses on the total reward or return obtained as a consequence of an agent's choices—specifically, how this return behaves from a probabilistic perspective. In this first comprehensive guide to distributional reinforcement learning, Marc G. Bellemare, Will Dabney, and Mark Rowland, who spearheaded development of the field, present its key concepts and review some of its many applications. They demonstrate its power to account for many complex, interesting phenomena that arise from interactions with one's environment. The authors present core ideas from classical reinforcement learning to contextualize distributional topics and include mathematical proofs pertaining to major results discussed in the text. They guide the reader through a series of algorithmic and mathematical developments that, in turn, characterize, compute, estimate, and make decisions on the basis of the random return. Practitioners in disciplines as diverse as finance (risk management), computational neuroscience, computational psychiatry, psychology, macroeconomics, and robotics are already using distributional reinforcement learning, paving the way for its expanding applications in mathematical finance, engineering, and the life sciences. More than a mathematical approach, distributional reinforcement learning represents a new perspective on how intelligent agents make predictions and decisions. |
| centralized training decentralized execution: Deep Reinforcement Learning and Its Industrial Use Cases Shubham Mahajan, Pethuru Raj, Amit Kant Pandit, 2024-10-29 This book serves as a bridge connecting the theoretical foundations of DRL with practical, actionable insights for implementing these technologies in a variety of industrial contexts, making it a valuable resource for professionals and enthusiasts at the forefront of technological innovation. Deep Reinforcement Learning (DRL) represents one of the most dynamic and impactful areas of research and development in the field of artificial intelligence. Bridging the gap between decision-making theory and powerful deep learning models, DRL has evolved from academic curiosity to a cornerstone technology driving innovation across numerous industries. Its core premise—enabling machines to learn optimal actions within complex environments through trial and error—has broad implications, from automating intricate decision processes to optimizing operations that were previously beyond the reach of traditional AI techniques. “Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications” is an essential guide for anyone eager to understand the nexus between cutting-edge artificial intelligence techniques and practical industrial applications. This book not only demystifies the complex theory behind deep reinforcement learning (DRL) but also provides a clear roadmap for implementing these advanced algorithms in a variety of industries to solve real-world problems. Through a careful blend of theoretical foundations, practical insights, and diverse case studies, the book offers a comprehensive look into how DRL is revolutionizing fields such as finance, healthcare, manufacturing, and more, by optimizing decisions in dynamic and uncertain environments. This book distills years of research and practical experience into accessible and actionable knowledge. Whether you’re an AI professional seeking to expand your toolkit, a business leader aiming to leverage AI for competitive advantage, or a student or academic researching the latest in AI applications, this book provides valuable insights and guidance. Beyond just exploring the successes of DRL, it critically examines challenges, pitfalls, and ethical considerations, preparing readers to not only implement DRL solutions but to do so responsibly and effectively. Audience The book will be read by researchers, postgraduate students, and industry engineers in machine learning and artificial intelligence, as well as those in business and industry seeking to understand how DRL can be applied to solve complex industry-specific challenges and improve operational efficiency. |
| centralized training decentralized execution: Computer Supported Cooperative Work and Social Computing Yuqing Sun, Tun Lu, Buqing Cao, Hongfei Fan, Dongning Liu, Bowen Du, Liping Gao, 2022-07-21 The two-volume set CCIS 1491 and 1492 constitutes the refereed post-conferenceproceedings of the 16th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2021, held in Xiangtan, China, November 26–28, 2021. The conference was held in a hybrid mode i.e. online and on-site in Xiangtan due to the COVID-19 crisis. The 65 revised full papers and 22 revised short papers were carefully reviewed and selected from 242 submissions. The papers are organized in the following topical sections: Volume I: Collaborative Mechanisms, Models, Approaches, Algorithms and Systems; Cooperative Evolutionary Computation and Human-like Intelligent Collaboration; Domain-Specific Collaborative Applications; Volume II: Crowd Intelligence and Crowd Cooperative Computing; Social Media and Online Communities. |
| centralized training decentralized execution: Theoretical Aspects of Software Engineering Cristina David, Meng Sun, 2023-06-26 This book constitutes the proceedings of the 17th International Conference on Theoretical Aspects of Software Engineering, TASE 2023, held in Bristol, UK, July 4–6, 2023. The 19 full papers and 2 short papers included in this book were carefully reviewed and selected from 49 submissions. They cover the following areas: distributed and concurrent systems; cyber-physical systems; embedded and real-time systems; object-oriented systems; quantum computing; formal verification and program semantics; static analysis; formal methods; verification and testing for AI systems; and AI for formal methods. |
| centralized training decentralized execution: Advanced Information Networking and Applications Leonard Barolli, Farookh Hussain, Tomoya Enokido, 2022-03-30 This book covers the theory, design and applications of computer networks, distributed computing and information systems. Networks of today are going through a rapid evolution, and there are many emerging areas of information networking and their applications. Heterogeneous networking supported by recent technological advances in low-power wireless communications along with silicon integration of various functionalities such as sensing, communications, intelligence and actuations is emerging as a critically important disruptive computer class based on a new platform, networking structure and interface that enable novel, low-cost and high-volume applications. Several of such applications have been difficult to realize because of many interconnections problems. To fulfill their large range of applications, different kinds of networks need to collaborate, and wired and next generation wireless systems should be integrated in order to develop high-performance computing solutions to problems arising from the complexities of these networks. The aim of the book “Advanced Information Networking and Applications” is to provide the latest research findings, innovative research results, methods and development techniques from both theoretical and practical perspectives related to the emerging areas of information networking and applications. |
| centralized training decentralized execution: Smart Applications with Advanced Machine Learning and Human-Centred Problem Design D. Jude Hemanth, Utku Kose, Junzo Watada, Bogdan Patrut, 2023-01-01 This book brings together the most recent, quality research papers accepted and presented in the 3rd International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2021) held in Antalya, Turkey between 1-3 October 2021. Objective of the content is to provide important and innovative research for developments-improvements within different engineering fields, which are highly interested in using artificial intelligence and applied mathematics. As a collection of the outputs from the ICAIAME 2021, the book is specifically considering research outcomes including advanced use of machine learning and careful problem designs on human-centred aspects. In this context, it aims to provide recent applications for real-world improvements making life easier and more sustainable for especially humans. The book targets the researchers, degree students, and practitioners from both academia and the industry. |
| centralized training decentralized execution: Agents and Artificial Intelligence Ana Paula Rocha, Luc Steels, Jaap van den Herik, 2022-07-18 This book constitutes selected papers from the refereed proceedings of the 13th International Conference on Agents and Artificial Intelligence, ICAART 2021, which was held online during February 4–6, 2021. A total of 72 full and 99 short papers were carefully reviewed and selected for the conference from a total of 298 submissions; 17 selected full papers are included in this book. They were organized in topical sections named agents and artificial intelligence. |
| centralized training decentralized execution: Advances in Guidance, Navigation and Control Liang Yan, Haibin Duan, Xiang Yu, 2021-11-12 This book features the latest theoretical results and techniques in the field of guidance, navigation, and control (GNC) of vehicles and aircraft. It covers a range of topics, including, but not limited to, intelligent computing communication and control; new methods of navigation, estimation, and tracking; control of multiple moving objects; manned and autonomous unmanned systems; guidance, navigation, and control of miniature aircraft; and sensor systems for guidance, navigation, and control. Presenting recent advances in the form of illustrations, tables, and text, it also provides detailed information of a number of the studies, to offer readers insights for their own research. In addition, the book addresses fundamental concepts and studies in the development of GNC, making it a valuable resource for both beginners and researchers wanting to further their understanding of guidance, navigation, and control. |
| centralized training decentralized execution: Fluidware Franco Zambonelli, |
| centralized training decentralized execution: Intelligent Computing Kohei Arai, 2023-10-02 This book is a collection of insightful and unique state-of the-art papers presented at the Computing Conference which took place in London on June 22–23, 2023. A total of 539 papers were received out of which 193 were selected for presenting after double-blind peer-review. The book covers a wide range of scientific topics including IoT, Artificial Intelligence, Computing, Data Science, Networking, Data security and Privacy, etc. The conference was successful in reaping the advantages of both online and offline modes. The goal of this conference is to give a platform to researchers with fundamental contributions and to be a premier venue for academic and industry practitioners to share new ideas and development experiences. We hope that readers find this book interesting and valuable. We also expect that the conference and its publications will be a trigger for further related research and technology improvements in this important subject. |
| centralized training decentralized execution: Neural Information Processing Mohammad Tanveer, Sonali Agarwal, Seiichi Ozawa, Asif Ekbal, Adam Jatowt, 2023-04-12 The three-volume set LNCS 13623, 13624, and 13625 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022. The 146 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications. The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements. |
| centralized training decentralized execution: Machine Learning Algorithms and Techniques Krishna Bonagiri, 2024-06-21 Machine Learning Algorithms and Techniques the concepts, popular algorithms, and essential techniques of machine learning. A comprehensive covering supervised, unsupervised, and reinforcement learning methods while exploring key algorithms like decision trees, neural networks, clustering, and more. Practical applications and examples bring each algorithm to life, helping readers understand how these models are used to solve real-world problems. Designed for both beginners and experienced practitioners, this book is an ideal guide for mastering the fundamentals and applications of machine learning. |
| centralized training decentralized execution: Reinforcement Learning Phil Winder Ph.D., 2020-11-06 Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcementand enable a machine to learn by itself. Author Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You'll explore the current state of RL, focus on industrial applications, learnnumerous algorithms, and benefit from dedicated chapters on deploying RL solutions to production. This is no cookbook; doesn't shy away from math and expects familiarity with ML. Learn what RL is and how the algorithms help solve problems Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning Dive deep into a range of value and policy gradient methods Apply advanced RL solutions such as meta learning, hierarchical learning, multi-agent, and imitation learning Understand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and more Get practical examples through the accompanying website |
| centralized training decentralized execution: Proceedings of 3rd 2023 International Conference on Autonomous Unmanned Systems (3rd ICAUS 2023) Yi Qu, |
| centralized training decentralized execution: Autonomous Agents and Multiagent Systems Gita Sukthankar, Juan A. Rodriguez-Aguilar, 2017-11-23 This book features a selection of best papers from 13 workshops held at the International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017, held in Sao Paulo, Brazil, in May 2017. The 17 full papers presented in this volume were carefully reviewed and selected for inclusion in this volume. They cover specific topics, both theoretical and applied, in the general area of autonomous agents and multiagent systems. |
| centralized training decentralized execution: Artificial Neural Networks and Machine Learning – ICANN 2021 Igor Farkaš, Paolo Masulli, Sebastian Otte, Stefan Wermter, 2021-09-10 The proceedings set LNCS 12891, LNCS 12892, LNCS 12893, LNCS 12894 and LNCS 12895 constitute the proceedings of the 30th International Conference on Artificial Neural Networks, ICANN 2021, held in Bratislava, Slovakia, in September 2021.* The total of 265 full papers presented in these proceedings was carefully reviewed and selected from 496 submissions, and organized in 5 volumes. In this volume, the papers focus on topics such as model compression, multi-task and multi-label learning, neural network theory, normalization and regularization methods, person re-identification, recurrent neural networks, and reinforcement learning. *The conference was held online 2021 due to the COVID-19 pandemic. |
| centralized training decentralized execution: Proceedings of 2023 7th Chinese Conference on Swarm Intelligence and Cooperative Control Xiaoduo Li, |
| centralized training decentralized execution: Towards Autonomous Robotic Systems Fumiya Iida, Perla Maiolino, Arsen Abdulali, Mingfeng Wang, 2023-09-07 This book constitutes the refereed proceedings of the 24th Annual Conference Towards Autonomous Robotic Systems, TAROS 2023, held in Cambridge, UK, during September 13–15, 2023. The 40 full papers presented in this book were carefully reviewed and selected from 70 submissions. They cover a wide range of different topics such as: agri-food robotics; autonomy; collaborative and service robotics; locomotion and manipulation; machine vision; multi-robot systems; soft robotics; tactile sensing; and teleoperation. |
| centralized training decentralized execution: Artificial Intelligence & Machine Learning Mrs. Haritha V, Mrs. G. Mareeswari, Prof. V. Kiran Kumar, Dr. Prerana Nilesh Khairnar, 2024-06-28 Artificial Intelligence & Machine Learning the fundamentals, advancements, and practical applications of AI and ML. Covering key concepts, algorithms, and tools Readers with insights into machine learning models, neural networks, natural language processing, and computer vision. Suitable for beginners and professionals alike, the book balances theory and hands-on examples to equip readers with the skills needed to design intelligent systems. It delves into ethical considerations and future trends, offering a comprehensive overview for anyone interested in understanding or developing AI and ML technologies. |
| centralized training decentralized execution: Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control Zhang Ren, Mengyi Wang, Yongzhao Hua, 2022-07-29 This book includes original, peer-reviewed research papers from the 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control (CCSICC2021), held in Shenzhen, China on January 19-22, 2022. The topics covered include but are not limited to: reviews and discussions of swarm intelligence, basic theories on swarm intelligence, swarm communication and networking, swarm perception, awareness and location, swarm decision and planning, cooperative control, cooperative guidance, swarm simulation and assessment. The papers showcased here share the latest findings on theories, algorithms and applications in swarm intelligence and cooperative control, making the book a valuable asset for researchers, engineers, and university students alike. |
CENTRALIZE Definition & Meaning - Merriam-Webster
The meaning of CENTRALIZE is to form a center : cluster around a center. How to use centralize in a sentence.
CENTRALIZED | English meaning - Cambridge Dictionary
CENTRALIZED definition: controlled by one main system or authority: . Learn more.
CENTRALIZED Definition & Meaning - Dictionary.com
existing in one place, or being the center point of a network: The system allows users to record subscriber complaints in a single database, creating a centralized source of information to …
CENTRALIZE definition and meaning | Collins English Dictionary
2 meanings: 1. to draw or move (something) to or towards a centre 2. to bring or come under central control, esp governmental.... Click for more definitions.
“Centralized” or “Centralised”—What's the difference? | Sapling
Centralized and centralised are both English terms. Usage Centralized is predominantly used in American (US) English ( en-US ) while centralised is predominantly used in British English …
Centralize Definition & Meaning | Britannica Dictionary
The city's hospitals hope to centralize [= consolidate] medical records in a single database. All shipping operations have been centralized at the Miami office. The controversial reforms could …
Centralized - definition of centralized by The Free Dictionary
centralized - drawn toward a center or brought under the control of a central authority; "centralized control of emergency relief efforts"; "centralized government"
CENTRALIZE Definition & Meaning - Merriam-Webster
The meaning of CENTRALIZE is to form a center : cluster around a center. How to use centralize in a sentence.
CENTRALIZED | English meaning - Cambridge Dictionary
CENTRALIZED definition: controlled by one main system or authority: . Learn more.
CENTRALIZED Definition & Meaning - Dictionary.com
existing in one place, or being the center point of a network: The system allows users to record subscriber complaints in a single database, creating a centralized source of information to assist …
CENTRALIZE definition and meaning | Collins English Dictionary
2 meanings: 1. to draw or move (something) to or towards a centre 2. to bring or come under central control, esp governmental.... Click for more definitions.
“Centralized” or “Centralised”—What's the difference? | Sapling
Centralized and centralised are both English terms. Usage Centralized is predominantly used in American (US) English ( en-US ) while centralised is predominantly used in British English (used …
Centralize Definition & Meaning | Britannica Dictionary
The city's hospitals hope to centralize [= consolidate] medical records in a single database. All shipping operations have been centralized at the Miami office. The controversial reforms could …
Centralized - definition of centralized by The Free Dictionary
centralized - drawn toward a center or brought under the control of a central authority; "centralized control of emergency relief efforts"; "centralized government"
