Download or read book Game Theoretic Learning and Distributed Optimization in Memoryless Multi Agent Systems written by Tatiana Tatarenko and published by Springer. This book was released on 2017-09-19 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Distributed Optimization in Multi agent Systems written by Salar Rahili and published by . This book was released on 2016 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: In last part of this dissertation, the distributed average tracking problem is addressed for a group of heterogeneous physical agents consisting of single-integrator, double-integrator and Euler-Lagrange dynamics. Here, the goal is that each agent uses local information and local interaction to calculate the average of individual time-varying reference inputs, one per agent. Dynamic average tracking is the main challenge in many other distributed algorithms, such as distributed optimization, and distributed Kalman filtering.
Download or read book Multi agent Optimization written by Angelia Nedić and published by Springer. This book was released on 2018-11-01 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence written by Nikos Vlassis and published by Morgan & Claypool Publishers. This book was released on 2007 with total page 85 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Distributed Optimization Game and Learning Algorithms written by Huiwei Wang and published by Springer. This book was released on 2021-02-05 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Interactions in Multiagent Systems Fairness Social Optimality and Individual Rationality written by Jianye Hao and published by Springer. This book was released on 2016-04-13 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Multiagent Systems written by Yoav Shoham and published by Cambridge University Press. This book was released on 2008-12-15 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Coordination of Large Scale Multiagent Systems written by Paul Scerri and published by Springer Science & Business Media. This book was released on 2005-10-17 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: Challenges arise when the size of a group of cooperating agents is scaled to hundreds or thousands of members. In domains such as space exploration, military and disaster response, groups of this size (or larger) are required to achieve extremely complex, distributed goals. To effectively and efficiently achieve their goals, members of a group need to cohesively follow a joint course of action while remaining flexible to unforeseen developments in the environment. Coordination of Large-Scale Multiagent Systems provides extensive coverage of the latest research and novel solutions being developed in the field. It describes specific systems, such as SERSE and WIZER, as well as general approaches based on game theory, optimization and other more theoretical frameworks. It will be of interest to researchers in academia and industry, as well as advanced-level students.
Download or read book Game Theoretic Control of Multi agent Systems written by Domenico Cappello and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Game Theory and Machine Learning for Cyber Security written by Charles A. Kamhoua and published by John Wiley & Sons. This book was released on 2021-09-08 with total page 546 pages. Available in PDF, EPUB and Kindle. Book excerpt: GAME THEORY AND MACHINE LEARNING FOR CYBER SECURITY Move beyond the foundations of machine learning and game theory in cyber security to the latest research in this cutting-edge field In Game Theory and Machine Learning for Cyber Security, a team of expert security researchers delivers a collection of central research contributions from both machine learning and game theory applicable to cybersecurity. The distinguished editors have included resources that address open research questions in game theory and machine learning applied to cyber security systems and examine the strengths and limitations of current game theoretic models for cyber security. Readers will explore the vulnerabilities of traditional machine learning algorithms and how they can be mitigated in an adversarial machine learning approach. The book offers a comprehensive suite of solutions to a broad range of technical issues in applying game theory and machine learning to solve cyber security challenges. Beginning with an introduction to foundational concepts in game theory, machine learning, cyber security, and cyber deception, the editors provide readers with resources that discuss the latest in hypergames, behavioral game theory, adversarial machine learning, generative adversarial networks, and multi-agent reinforcement learning. Readers will also enjoy: A thorough introduction to game theory for cyber deception, including scalable algorithms for identifying stealthy attackers in a game theoretic framework, honeypot allocation over attack graphs, and behavioral games for cyber deception An exploration of game theory for cyber security, including actionable game-theoretic adversarial intervention detection against advanced persistent threats Practical discussions of adversarial machine learning for cyber security, including adversarial machine learning in 5G security and machine learning-driven fault injection in cyber-physical systems In-depth examinations of generative models for cyber security Perfect for researchers, students, and experts in the fields of computer science and engineering, Game Theory and Machine Learning for Cyber Security is also an indispensable resource for industry professionals, military personnel, researchers, faculty, and students with an interest in cyber security.
Download or read book Probability Collectives written by Anand Jayant Kulkarni and published by Springer. This book was released on 2015-02-25 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an emerging computational intelligence tool in the framework of collective intelligence for modeling and controlling distributed multi-agent systems referred to as Probability Collectives. In the modified Probability Collectives methodology a number of constraint handling techniques are incorporated, which also reduces the computational complexity and improved the convergence and efficiency. Numerous examples and real world problems are used for illustration, which may also allow the reader to gain further insight into the associated concepts.
Download or read book Distributed Optimization in Multi agent Systems Applications to Distributed Regression written by Sundhar Ram Srinivasan and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The context for this work is cooperative multi-agent systems (MAS). An agent is an intelligent entity that can measure some aspect of its environment, process information and possibly influence the environment through its action. A cooperative MAS can be defined as a loosely coupled network of agents that interact and cooperate to solve problems that are beyond the individual capabilities or knowledge of each agent. The focus of this thesis is distributed stochastic optimization in multi-agent systems. In distributed optimization, the complete optimization problem is not available at a single location but is distributed among different agents. The distributed optimization problem is additionally stochastic when the information available to each agent is with stochastic errors. Communication constraints, lack of global information about the network topology and the absence of coordinating agents make it infeasible to collect all the information at a single location and then treat it as a centralized optimization problem. Thus, the problem has to be solved using algorithms that are distributed, i.e., different parts of the algorithm are executed at different agents, and local, i.e., each agent uses only information locally available to it and other information it can obtain from its immediate neighbors. In this thesis, we will primarily focus on the specific problem of minimizing a sum of functions over a constraint set, when each component function is known partially (with stochastic errors) to a unique agent. The constraint set is known to all the agents. We propose three distributed and local algorithms, establish asymptotic convergence with diminishing stepsizes and obtain rate of convergence results. Stochastic errors, as we will see, arise naturally when the objective function known to an agent has a random variable with unknown statistics. Additionally, stochastic errors also model communication and quantization errors. The problem is motivated by distributed regression in sensor networks and power control in cellular systems. We also discuss an important extension to the above problem. In the extension, the network goal is to minimize a global function of a sum of component functions over a constraint set. Each component function is known to a unique network agent. The global function and the constraint set are known to all the agents. Unlike the previous problem, this problem is not stochastic. However, the objective function in this problem is more general. We propose an algorithm to solve this problem and establish its convergence.
Download or read book Decentralised Reinforcement Learning in Markov Games written by Peter Vrancx and published by ASP / VUBPRESS / UPA. This book was released on 2011 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introducing a new approach to multiagent reinforcement learning and distributed artificial intelligence, this guide shows how classical game theory can be used to compose basic learning units. This approach to creating agents has the advantage of leading to powerful, yet intuitively simple, algorithms that can be analyzed. The setup is demonstrated here in a number of different settings, with a detailed analysis of agent learning behaviors provided for each. A review of required background materials from game theory and reinforcement learning is also provided, along with an overview of related multiagent learning methods.
Download or read book Contributions to Game theoretic Aspects of Multi agent Systems written by Ryan W. Porter and published by . This book was released on 2004 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Distributed Optimization of Nonconvex Multiagent Systems written by Peiran Song and published by . This book was released on 2015 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: The focus of this dissertation is to provide aunified and efficient solution method to the general nonconvex optimization problems arisefrom the multiagent systems. A new class of distributed and parallel algorithms with provable convergence to local optimal solutions of the nonconvex problem is proposed. More specifically, in this work the study of the nonconvex optimization problem is gradually generalized starting from nonconvex objective function but convex privative constraints and ending up with the general formulation including nonconvex coupling constraints. First, we propose a novel decomposition framework for the distributed optimizationof general nonconvex sum-utility functions arising naturallyfrom the design of wireless multi-user interfering systems. The first class of (inexact) Jacobi best-response algorithms with provable convergenceis derived, where all the users simultaneously and iteratively solve a suitablyconvexified version of the original sum-utility optimization problem. It can be interpreted as a general dynamic pricing mechanism which providesa unified view of existing pricing schemes that are based on heuristics. The proposed algorithmic framework can be easily particularizedto well-known applications, giving rise to very efficient practical(Jacobi or Gauss-Seidel) algorithms that outperform existing ad-hocmethods proposed for very specific problems. Then, the problem formulation is generalized to allow the existence of convex coupling constraints in the feasible set. By choosing properly the convex approximates, a new class of distributed Successive Convex Approximation (SCA)-based algorithms are proposed hinging on the primal and dual decomposition techniques. Finally, a general algorithmic frameworkfor the minimization of a nonconvex smooth objective function subject to nonconvexsmooth constraints is proposed. The algorithm solves a sequence of (separable) strongly convex problems and maintains feasibility at each iteration. Convergence to a stationary solution of the original nonconvex optimization problem is established. Our frameworkis very general and flexible; it unifies several existing SCA-based algorithms such as (proximal) gradientor Newton type methods, block coordinate (parallel) descent schemes, D.C.-function methods, and improves on their convergenceproperties. More importantly, and differently from current SCA approaches, it naturally leads to distributed and parallelizable implementationsfor a large class of nonconvex problems.
Download or read book Game Theory and Decision Theory in Agent Based Systems written by Simon D Parsons and published by . This book was released on 2002-06-01 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Learning in Cooperative Multi Agent Systems written by Thomas Gabel and published by Sudwestdeutscher Verlag Fur Hochschulschriften AG. This book was released on 2009-09 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a distributed system, a number of individually acting agents coexist. In order to achieve a common goal, coordinated cooperation between the agents is crucial. Many real-world applications are well-suited to be formulated in terms of spatially or functionally distributed entities. Job-shop scheduling represents one such application. Multi-agent reinforcement learning (RL) methods allow for automatically acquiring cooperative policies based solely on a specification of the desired joint behavior of the whole system. However, the decentralization of the control and observation of the system among independent agents has a significant impact on problem complexity. The author Thomas Gabel addresses the intricacy of learning and acting in multi-agent systems by two complementary approaches. He identifies a subclass of general decentralized decision-making problems that features provably reduced complexity. Moreover, he presents various novel model-free multi-agent RL algorithms that are capable of quickly obtaining approximate solutions in the vicinity of the optimum. All algorithms proposed are evaluated in the scope of various established scheduling benchmark problems.
