Game Theoretic Learning And Distributed Optimization In Memoryless Multi Agent Systems
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Game Theoretic Learning And Distributed Optimization In Memoryless Multi Agent Systems
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Author : Tatiana Tatarenko
language : en
Publisher: Springer
Release Date : 2017-09-19
Game Theoretic Learning And Distributed Optimization In Memoryless Multi Agent Systems written by Tatiana Tatarenko and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-19 with Science categories.
This book presents new efficient methods for optimization in realistic large-scale, multi-agent systems. These methods do not require the agents to have the full information about the system, but instead allow them to make their local decisions based only on the local information, possibly obtained during communication with their local neighbors. The book, primarily aimed at researchers in optimization and control, considers three different information settings in multi-agent systems: oracle-based, communication-based, and payoff-based. For each of these information types, an efficient optimization algorithm is developed, which leads the system to an optimal state. The optimization problems are set without such restrictive assumptions as convexity of the objective functions, complicated communication topologies, closed-form expressions for costs and utilities, and finiteness of the system’s state space.
Distributed Optimization Based Control Of Multi Agent Networks In Complex Environments
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Author : Minghui Zhu
language : en
Publisher: Springer
Release Date : 2015-06-11
Distributed Optimization Based Control Of Multi Agent Networks In Complex Environments written by Minghui Zhu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-11 with Technology & Engineering categories.
This book offers a concise and in-depth exposition of specific algorithmic solutions for distributed optimization based control of multi-agent networks and their performance analysis. It synthesizes and analyzes distributed strategies for three collaborative tasks: distributed cooperative optimization, mobile sensor deployment and multi-vehicle formation control. The book integrates miscellaneous ideas and tools from dynamic systems, control theory, graph theory, optimization, game theory and Markov chains to address the particular challenges introduced by such complexities in the environment as topological dynamics, environmental uncertainties, and potential cyber-attack by human adversaries. The book is written for first- or second-year graduate students in a variety of engineering disciplines, including control, robotics, decision-making, optimization and algorithms and with backgrounds in aerospace engineering, computer science, electrical engineering, mechanical engineering and operations research. Researchers in these areas may also find the book useful as a reference.
Distributed Optimization Game And Learning Algorithms
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Author : Huiwei Wang
language : en
Publisher: Springer Nature
Release Date : 2021-01-04
Distributed Optimization Game And Learning Algorithms written by Huiwei Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-04 with Technology & Engineering categories.
This book provides the fundamental theory of distributed optimization, game and learning. It includes those working directly in optimization,-and also many other issues like time-varying topology, communication delay, equality or inequality constraints,-and random projections. This book is meant for the researcher and engineer who uses distributed optimization, game and learning theory in fields like dynamic economic dispatch, demand response management and PHEV routing of smart grids.
Multi Agent Optimization
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Author : Angelia Nedić
language : en
Publisher: Springer
Release Date : 2018-11-01
Multi Agent Optimization written by Angelia Nedić and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-01 with Business & Economics categories.
This book contains three well-written research tutorials that inform the graduate reader about the forefront of current research in multi-agent optimization. These tutorials cover topics that have not yet found their way in standard books and offer the reader the unique opportunity to be guided by major researchers in the respective fields. Multi-agent optimization, lying at the intersection of classical optimization, game theory, and variational inequality theory, is at the forefront of modern optimization and has recently undergone a dramatic development. It seems timely to provide an overview that describes in detail ongoing research and important trends. This book concentrates on Distributed Optimization over Networks; Differential Variational Inequalities; and Advanced Decomposition Algorithms for Multi-agent Systems. This book will appeal to both mathematicians and mathematically oriented engineers and will be the source of inspiration for PhD students and researchers.
A Concise Introduction To Multiagent Systems And Distributed Artificial Intelligence
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Author : Nikos Vlassis
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2007
A Concise Introduction To Multiagent Systems And Distributed Artificial Intelligence written by Nikos Vlassis and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Computers categories.
Multiagent systems is an expanding field that blends classical fields like game theory and decentralized control with modern fields like computer science and machine learning. This monograph provides a concise introduction to the subject, covering the theoretical foundations as well as more recent developments in a coherent and readable manner. The text is centered on the concept of an agent as decision maker. Chapter 1 is a short introduction to the field of multiagent systems. Chapter 2 covers the basic theory of singleagent decision making under uncertainty. Chapter 3 is a brief introduction to game theory, explaining classical concepts like Nash equilibrium. Chapter 4 deals with the fundamental problem of coordinating a team of collaborative agents. Chapter 5 studies the problem of multiagent reasoning and decision making under partial observability. Chapter 6 focuses on the design of protocols that are stable against manipulations by self-interested agents. Chapter 7 provides a short introduction to the rapidly expanding field of multiagent reinforcement learning. The material can be used for teaching a half-semester course on multiagent systems covering, roughly, one chapter per lecture.
Distributed Optimization And Learning
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Author : Zhongguo Li
language : en
Publisher: Academic Press
Release Date : 2024-08-01
Distributed Optimization And Learning written by Zhongguo Li and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-01 with Technology & Engineering categories.
Distributed Optimization and Learning: A Control-Theoretic Perspective illustrates the underlying principles of distributed optimization and learning. The book presents a systematic and self-contained description of distributed optimization and learning algorithms from a control-theoretic perspective. It focuses on exploring control-theoretic approaches and how those approaches can be utilized to solve distributed optimization and learning problems over network-connected, multi-agent systems. As there are strong links between optimization and learning, this book provides a unified platform for understanding distributed optimization and learning algorithms for different purposes.
Multiagent Systems
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Author : Yoav Shoham
language : en
Publisher: Cambridge University Press
Release Date : 2008-12-15
Multiagent Systems written by Yoav Shoham and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-12-15 with Computers categories.
This exciting and pioneering new overview of multiagent systems, which are online systems composed of multiple interacting intelligent agents, i.e., online trading, offers a newly seen computer science perspective on multiagent systems, while integrating ideas from operations research, game theory, economics, logic, and even philosophy and linguistics. The authors emphasize foundations to create a broad and rigorous treatment of their subject, with thorough presentations of distributed problem solving, game theory, multiagent communication and learning, social choice, mechanism design, auctions, cooperative game theory, and modal logics of knowledge and belief. For each topic, basic concepts are introduced, examples are given, proofs of key results are offered, and algorithmic considerations are examined. An appendix covers background material in probability theory, classical logic, Markov decision processes and mathematical programming. Written by two of the leading researchers of this engaging field, this book will surely serve as THE reference for researchers in the fastest-growing area of computer science, and be used as a text for advanced undergraduate or graduate courses.
Handbook Of Reinforcement Learning And Control
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Author : Kyriakos G. Vamvoudakis
language : en
Publisher: Springer Nature
Release Date : 2021-06-23
Handbook Of Reinforcement Learning And Control written by Kyriakos G. Vamvoudakis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-23 with Technology & Engineering categories.
This handbook presents state-of-the-art research in reinforcement learning, focusing on its applications in the control and game theory of dynamic systems and future directions for related research and technology. The contributions gathered in this book deal with challenges faced when using learning and adaptation methods to solve academic and industrial problems, such as optimization in dynamic environments with single and multiple agents, convergence and performance analysis, and online implementation. They explore means by which these difficulties can be solved, and cover a wide range of related topics including: deep learning; artificial intelligence; applications of game theory; mixed modality learning; and multi-agent reinforcement learning. Practicing engineers and scholars in the field of machine learning, game theory, and autonomous control will find the Handbook of Reinforcement Learning and Control to be thought-provoking, instructive and informative.
Learning And Adaption In Multi Agent Systems
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Author : Karl Tuyls
language : en
Publisher: Springer
Release Date : 2006-03-07
Learning And Adaption In Multi Agent Systems written by Karl Tuyls and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-03-07 with Computers categories.
This book constitutes the thoroughly refereed post-proceedings of the First International Workshop on Learning and Adaption in Multi-Agent Systems, LAMAS 2005, held in The Netherlands, in July 2005, as an associated event of AAMAS 2005. The 13 revised papers presented together with two invited talks were carefully reviewed and selected from the lectures given at the workshop.
Interactions In Multiagent Systems Fairness Social Optimality And Individual Rationality
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Author : Jianye Hao
language : en
Publisher: Springer
Release Date : 2016-04-13
Interactions In Multiagent Systems Fairness Social Optimality And Individual Rationality written by Jianye Hao and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-13 with Technology & Engineering categories.
This book mainly aims at solving the problems in both cooperative and competitive multi-agent systems (MASs), exploring aspects such as how agents can effectively learn to achieve the shared optimal solution based on their local information and how they can learn to increase their individual utility by exploiting the weakness of their opponents. The book describes fundamental and advanced techniques of how multi-agent systems can be engineered towards the goal of ensuring fairness, social optimality, and individual rationality; a wide range of further relevant topics are also covered both theoretically and experimentally. The book will be beneficial to researchers in the fields of multi-agent systems, game theory and artificial intelligence in general, as well as practitioners developing practical multi-agent systems.