Effective Learning In Non Stationary Multiagent Environments
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Effective Learning In Non Stationary Multiagent Environments
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Author : Dong Ki Kim (Artificial intelligence expert)
language : en
Publisher:
Release Date : 2023
Effective Learning In Non Stationary Multiagent Environments written by Dong Ki Kim (Artificial intelligence expert) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Multiagent reinforcement learning (MARL) provides a principled framework for a group of artificial intelligence agents to learn collaborative and/or competitive behaviors at the level of human experts. Multiagent learning settings inherently solve much more complex problems than single-agent learning because an agent interacts both with the environment and other agents. In particular, multiple agents simultaneously learn in MARL, leading to natural non-stationarity in the experiences encountered and thus requiring each agent to its behavior with respect to potentially large changes in other agents' policies. This thesis aims to address the non-stationarity challenge in multiagent learning from three important topics: 1) adaptation, 2) convergence, and 3) state space. The first topic answers how an agent can learn effective adaptation strategies concerning other agents' changing policies by developing a new meta-learning framework. The second topic answers how agents can adapt and influence the joint learning process such that policies converge to more desirable limiting behaviors by the end of learning based on a new game-theoretical solution concept. Lastly, the last topic answers how state space size can be reduced based on knowledge sharing and context-specific abstraction such that the learning complexity is less affected by non-stationarity. In summary, this thesis develops theoretical and algorithmic contributions to provide principled answers to the aforementioned topics on non-stationarity. The developed algorithms in this thesis demonstrate their effectiveness in a diverse suite of multiagent benchmark domains, including the full spectrum of mixed incentive, competitive, and cooperative environments.
Sample Efficient Multiagent Learning In The Presence Of Markovian Agents
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Author : Doran Chakraborty
language : en
Publisher: Springer
Release Date : 2013-09-30
Sample Efficient Multiagent Learning In The Presence Of Markovian Agents written by Doran Chakraborty and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-09-30 with Technology & Engineering categories.
The problem of Multiagent Learning (or MAL) is concerned with the study of how intelligent entities can learn and adapt in the presence of other such entities that are simultaneously adapting. The problem is often studied in the stylized settings provided by repeated matrix games (a.k.a. normal form games). The goal of this book is to develop MAL algorithms for such a setting that achieve a new set of objectives which have not been previously achieved. In particular this book deals with learning in the presence of a new class of agent behavior that has not been studied or modeled before in a MAL context: Markovian agent behavior. Several new challenges arise when interacting with this particular class of agents. The book takes a series of steps towards building completely autonomous learning algorithms that maximize utility while interacting with such agents. Each algorithm is meticulously specified with a thorough formal treatment that elucidates its key theoretical properties.
Ecai 2023
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Author : K. Gal
language : en
Publisher: IOS Press
Release Date : 2023-10-18
Ecai 2023 written by K. Gal and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-18 with Computers categories.
Artificial intelligence, or AI, now affects the day-to-day life of almost everyone on the planet, and continues to be a perennial hot topic in the news. This book presents the proceedings of ECAI 2023, the 26th European Conference on Artificial Intelligence, and of PAIS 2023, the 12th Conference on Prestigious Applications of Intelligent Systems, held from 30 September to 4 October 2023 and on 3 October 2023 respectively in Kraków, Poland. Since 1974, ECAI has been the premier venue for presenting AI research in Europe, and this annual conference has become the place for researchers and practitioners of AI to discuss the latest trends and challenges in all subfields of AI, and to demonstrate innovative applications and uses of advanced AI technology. ECAI 2023 received 1896 submissions – a record number – of which 1691 were retained for review, ultimately resulting in an acceptance rate of 23%. The 390 papers included here, cover topics including machine learning, natural language processing, multi agent systems, and vision and knowledge representation and reasoning. PAIS 2023 received 17 submissions, of which 10 were accepted after a rigorous review process. Those 10 papers cover topics ranging from fostering better working environments, behavior modeling and citizen science to large language models and neuro-symbolic applications, and are also included here. Presenting a comprehensive overview of current research and developments in AI, the book will be of interest to all those working in the field.
Advances In Learning Classifier Systems
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Author : Pier L. Lanzi
language : en
Publisher: Springer
Release Date : 2003-07-31
Advances In Learning Classifier Systems written by Pier L. Lanzi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-07-31 with Computers categories.
Learning classi er systems are rule-based systems that exploit evolutionary c- putation and reinforcement learning to solve di cult problems. They were - troduced in 1978 by John H. Holland, the father of genetic algorithms, and since then they have been applied to domains as diverse as autonomous robotics, trading agents, and data mining. At the Second International Workshop on Learning Classi er Systems (IWLCS 99), held July 13, 1999, in Orlando, Florida, active researchers reported on the then current state of learning classi er system research and highlighted some of the most promising research directions. The most interesting contri- tions to the meeting are included in the book Learning Classi er Systems: From Foundations to Applications, published as LNAI 1813 by Springer-Verlag. The following year, the Third International Workshop on Learning Classi er Systems (IWLCS 2000), held September 15{16 in Paris, gave participants the opportunity to discuss further advances in learning classi er systems. We have included in this volume revised and extended versions of thirteen of the papers presented at the workshop.
Advances In Neural Information Processing Systems 16
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Author : Sebastian Thrun
language : en
Publisher: MIT Press
Release Date : 2004
Advances In Neural Information Processing Systems 16 written by Sebastian Thrun and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Computers categories.
Papers presented at the 2003 Neural Information Processing Conference by leading physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The annual Neural Information Processing (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees -- physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications. Only thirty percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains all the papers presented at the 2003 conference.
Reinforcement Learning Unlocked A Deep Dive Into Advanced Methodologies
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Author : Adam Jones
language : en
Publisher: Walzone Press
Release Date : 2025-01-04
Reinforcement Learning Unlocked A Deep Dive Into Advanced Methodologies written by Adam Jones and has been published by Walzone Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-04 with Computers categories.
"Reinforcement Learning Unlocked: A Deep Dive into Advanced Methodologies" is an essential resource for those eager to enhance their mastery in reinforcement learning (RL). This in-depth book comprehensively covers the foundational concepts and theoretical aspects of RL, progressing to the latest techniques and transformative applications shaping the future of artificial intelligence. Delve into chapters that unravel the intricacies of RL, providing detailed exploration of model-based and model-free methodologies, deep reinforcement learning, policy gradient techniques, advanced exploration methods, multi-agent systems, and the empowering capabilities of transfer and meta-learning. Ideal for graduate students, researchers, and industry professionals, this book offers a thorough dive into the strategies that drive intelligent decision-making in dynamic, uncertain environments. With lucid explanations, algorithmic insights, and practical illustrations, readers will gain a nuanced understanding of constructing sophisticated RL models adept at tackling real-world challenges. Embark on a journey with "Reinforcement Learning Unlocked: A Deep Dive into Advanced Methodologies" and unlock the potential to innovate and excel in the ever-evolving field of AI.
Deep Learning In Natural Language Processing
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Author : Li Deng
language : en
Publisher: Springer
Release Date : 2018-05-23
Deep Learning In Natural Language Processing written by Li Deng and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-23 with Computers categories.
In recent years, deep learning has fundamentally changed the landscapes of a number of areas in artificial intelligence, including speech, vision, natural language, robotics, and game playing. In particular, the striking success of deep learning in a wide variety of natural language processing (NLP) applications has served as a benchmark for the advances in one of the most important tasks in artificial intelligence. This book reviews the state of the art of deep learning research and its successful applications to major NLP tasks, including speech recognition and understanding, dialogue systems, lexical analysis, parsing, knowledge graphs, machine translation, question answering, sentiment analysis, social computing, and natural language generation from images. Outlining and analyzing various research frontiers of NLP in the deep learning era, it features self-contained, comprehensive chapters written by leading researchers in the field. A glossary of technical terms and commonly used acronyms in the intersection of deep learning and NLP is also provided. The book appeals to advanced undergraduate and graduate students, post-doctoral researchers, lecturers and industrial researchers, as well as anyone interested in deep learning and natural language processing.
Autonomous Agents And Multiagent Systems
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Author : Gita Sukthankar
language : en
Publisher: Springer
Release Date : 2017-11-23
Autonomous Agents And Multiagent Systems written by Gita Sukthankar and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-23 with Computers categories.
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.
Prima 2013 Principles And Practice Of Multi Agent Systems
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Author : Guido Boella
language : en
Publisher: Springer
Release Date : 2013-11-19
Prima 2013 Principles And Practice Of Multi Agent Systems written by Guido Boella and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-19 with Computers categories.
This book constitutes the refereed proceedings of the 16th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2013, held in Dunedin, New Zealand, in December 2013. The conference was co-located with the 26th Australasian Artificial International Conference, AI 2013. The 24 revised full papers presented together with 18 short papers and 2 invited papers were carefully reviewed and selected from 81 submissions. The papers are organized in topical sections on foundations of agents and multi-agent systems; agent and multi-agent system architectures; agent-oriented software engineering; agent-based modelling and simulation; cooperation/collaboration, coordination/communication; hybrid technologies, application domains; and applications.
Agent And Multi Agent Systems Technologies And Applications
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Author : Piotr Jedrzejowicz
language : en
Publisher: Springer
Release Date : 2010-06-18
Agent And Multi Agent Systems Technologies And Applications written by Piotr Jedrzejowicz and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-06-18 with Computers categories.
This book constitutes the proceedings of the 4th KES International Symposium on Agent and Multi-Agent Systems, KES-AMSTA 2010, held in June 2010 in Gdynia, Poland. The discussed field is concerned with the development and analysis of AI-based problem-solving and control architectures for both single-agent and multiple-agent systems. Only 83 papers were selected for publication in both volumes and focus on topics such as: Multi-Agent Systems Design and Implementation, Negotiations and Social Issues, Web Services and Semantic Web, Cooperation, Coordination and Teamwork, Agent-Based Modeling, Simulation and Decision Making, Multi-Agent Applications, Management and e-Business, Mobile Agents and Robots, and Machine Learning.