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Multi Agent Online Decision Making With Imperfect Feedback
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Book Synopsis Multi-agent Online Decision Making with Imperfect Feedback by : Zhengyuan Zhou
Download or read book Multi-agent Online Decision Making with Imperfect Feedback written by Zhengyuan Zhou and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Data-driven decision making, lying at the intersection between learning and decision making, has emerged as an important paradigm for engineering, data science and operations research at large. This thesis considers one facet of data-driven decision making, where multiple agents engage in an online learning process and make sequential decisions using data that become available over time. More specifically, we consider a model of multi-agent online strategic decision making, in which the reward structures of agents are given by a general continuous game and the feedback information to each agent is imperfect in one or more ways: each agent's feedback may suffer from some combination of noise corruption, delays and loss. The thesis presents an in-depth inquiry into the last-iterate convergence to Nash equilibria in the presence of such imperfect feedback, when each agent utilizes a no-regret online learning algorithm to maximize its cumulative performance. Last-iterate convergence (i.e. convergence of the actual joint action of all agents) stands in contrast with the more traditionally-studied time-average convergence in the existing literature (i.e. convergence of the time-average of the historical joint actions) and provides a more relevant (albeit more challenging) metric for online decision making problems. Unfortunately, last-iterate convergence (particularly when imperfect feedback is present) is under-explored in the existing work in multi-agent online learning. Rising to this challenge, this thesis aims to bridge the existing gap by answering some of the open questions in this field. In particular, a key high-level insight this thesis aims to articulate is that a broad family of no-regret learning algorithms, known as online mirror descent, can be adapted in multi-agent learning to guarantee last-iterate convergence to Nash equilibria in a general class of games under severely imperfect feedback information. Further, using power control in wireless networks as a motivating application domain, this thesis then harnesses these adapted online learning algorithms and theoretical convergence results to design robust and low-overhead distributed algorithms that operate in realistic environments and that come with strong performance guarantees. In sum, this thesis contributes to the broad landscape of multi-agent online learning by, among other things, making clear that the ambitious agenda of last-iterate convergence is not out of reach and should be the new norm (rather than the exception) for judging an algorithm's performance. A second (at least equally important) perspective that the thesis contributes pertains to distributed algorithm design in control and/or optimization: the often more complex process of developing robust distributed algorithms to achieve a desired/optimal system state can be transformed into the static and often simpler process of designing a game. With this transformation, all the algorithms and convergence results in multi-agent online learning can be immediately harnessed to yield practical algorithms for the problem at hand. This viewpoint has the potential to simplify the algorithm designer's task, whatever domain it may be.
Author :Tatiana Valentine Guy Publisher :Springer Science & Business Media ISBN 13 :3642246478 Total Pages :207 pages Book Rating :4.6/5 (422 download)
Book Synopsis Decision Making with Imperfect Decision Makers by : Tatiana Valentine Guy
Download or read book Decision Making with Imperfect Decision Makers written by Tatiana Valentine Guy and published by Springer Science & Business Media. This book was released on 2011-11-13 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: Prescriptive Bayesian decision making has reached a high level of maturity and is well-supported algorithmically. However, experimental data shows that real decision makers choose such Bayes-optimal decisions surprisingly infrequently, often making decisions that are badly sub-optimal. So prevalent is such imperfect decision-making that it should be accepted as an inherent feature of real decision makers living within interacting societies. To date such societies have been investigated from an economic and gametheoretic perspective, and even to a degree from a physics perspective. However, little research has been done from the perspective of computer science and associated disciplines like machine learning, information theory and neuroscience. This book is a major contribution to such research. Some of the particular topics addressed include: How should we formalise rational decision making of a single imperfect decision maker? Does the answer change for a system of imperfect decision makers? Can we extend existing prescriptive theories for perfect decision makers to make them useful for imperfect ones? How can we exploit the relation of these problems to the control under varying and uncertain resources constraints as well as to the problem of the computational decision making? What can we learn from natural, engineered, and social systems to help us address these issues?
Book Synopsis Effective Online Decision-making in Complex Multi-agent Systems by : Theodoros Lykouris
Download or read book Effective Online Decision-making in Complex Multi-agent Systems written by Theodoros Lykouris and published by . This book was released on 2019 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: The emergence of online marketplaces has introduced important new dimensions to online decision-making. Classical algorithms developed to guarantee worst-case performance often focus strongly on the worst case; in typical inputs one can perform much better which makes these approaches not practical. Moreover, these marketplaces serve multiple agents who interact in complex ways; this adds important facets to designing online decision making approaches in these systems. This thesis aims to shed light on both of these issues. In particular, in the first theme of the thesis, we show how to utilize nice structures in the data to enhance classical worst-case guarantees without requiring that these structures are perfectly present. Instead the performance gracefully degrades as these structures become less present. We discuss how to exploit three such nice structures: existence of a really good alternative, well-behaved randomness, and predictability of future requests. The second theme of the thesis explores the multi-agent aspect of modern online decision-making which adds important constraints to the classical tasks. In this direction, we discuss pricing under the existence of network externalities (such as ones arising in ridesharing systems), outcomes in evolving game settings with multiple strategic learning agents, and tradeoffs between effective online decision-making and ethical goals regarding non-discrimination.
Book Synopsis Multi-Objective Decision Making by : Diederik M. Roijers
Download or read book Multi-Objective Decision Making written by Diederik M. Roijers and published by Morgan & Claypool Publishers. This book was released on 2017-04-20 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many real-world decision problems have multiple objectives. For example, when choosing a medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize the side effects. These objectives typically conflict, e.g., we can often increase the efficacy of the treatment, but at the cost of more severe side effects. In this book, we outline how to deal with multiple objectives in decision-theoretic planning and reinforcement learning algorithms. To illustrate this, we employ the popular problem classes of multi-objective Markov decision processes (MOMDPs) and multi-objective coordination graphs (MO-CoGs). First, we discuss different use cases for multi-objective decision making, and why they often necessitate explicitly multi-objective algorithms. We advocate a utility-based approach to multi-objective decision making, i.e., that what constitutes an optimal solution to a multi-objective decision problem should be derived from the available information about user utility. We show how different assumptions about user utility and what types of policies are allowed lead to different solution concepts, which we outline in a taxonomy of multi-objective decision problems. Second, we show how to create new methods for multi-objective decision making using existing single-objective methods as a basis. Focusing on planning, we describe two ways to creating multi-objective algorithms: in the inner loop approach, the inner workings of a single-objective method are adapted to work with multi-objective solution concepts; in the outer loop approach, a wrapper is created around a single-objective method that solves the multi-objective problem as a series of single-objective problems. After discussing the creation of such methods for the planning setting, we discuss how these approaches apply to the learning setting. Next, we discuss three promising application domains for multi-objective decision making algorithms: energy, health, and infrastructure and transportation. Finally, we conclude by outlining important open problems and promising future directions.
Book Synopsis Decision Making in Multi-agent Systems by : Karen Arman Pivazyan
Download or read book Decision Making in Multi-agent Systems written by Karen Arman Pivazyan and published by . This book was released on 2004 with total page 63 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multi-Agent Systems by : Marija Slavkovik
Download or read book Multi-Agent Systems written by Marija Slavkovik and published by Springer. This book was released on 2019-02-14 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the revised post-conference proceedings of the 16th European Conference on Multi-Agent Systems, EUMAS 2018, held at Bergen, Norway, in December 2018. The 18 full papers presented in this volume were carefully reviewed and selected from a total of 34 submissions. The papers report on both early and mature research and cover a wide range of topics in the field of multi-agent systems.
Book Synopsis Decision Theory and Multi-Agent Planning by : Giacomo Della Riccia
Download or read book Decision Theory and Multi-Agent Planning written by Giacomo Della Riccia and published by Springer Science & Business Media. This book was released on 2007-05-03 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: The work presents a modern, unified view on decision support and planning by considering its basics like preferences, belief, possibility and probability as well as utilities. These features together are immanent for software agents to believe the user that the agents are "intelligent".
Book Synopsis Large Group Decision Making by : Iván Palomares Carrascosa
Download or read book Large Group Decision Making written by Iván Palomares Carrascosa and published by Springer. This book was released on 2018-10-31 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: This SpringerBrief provides a pioneering, central point of reference for the interested reader in Large Group Decision Making trends such as consensus support, fusion and weighting of relevant decision information, subgroup clustering, behavior management, and implementation of decision support systems, among others. Based on the challenges and difficulties found in classical approaches to handle large decision groups, the principles, families of techniques, and newly related disciplines to Large-Group Decision Making (such as Data Science, Artificial Intelligence, Social Network Analysis, Opinion Dynamics, Behavioral and Cognitive Sciences), are discussed. Real-world applications and future directions of research on this novel topic are likewise highlighted.
Book Synopsis Special Issue on Multi-agent Dynamic Decision Making and Learning by : Konstantin Avrachenkov
Download or read book Special Issue on Multi-agent Dynamic Decision Making and Learning written by Konstantin Avrachenkov and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Local Decision-making in Multi-agent Systems by : Maike Kaufman
Download or read book Local Decision-making in Multi-agent Systems written by Maike Kaufman and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Models of Multi-agent Decision Making by : Julian Zappala
Download or read book Models of Multi-agent Decision Making written by Julian Zappala and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Special Issue: Multi-agent Decision Making by :
Download or read book Special Issue: Multi-agent Decision Making written by and published by . This book was released on 2014 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Distributed Decision-making of Networked Multi-agent Systems in Complex Environments by : Minghui Zhu
Download or read book Distributed Decision-making of Networked Multi-agent Systems in Complex Environments written by Minghui Zhu and published by . This book was released on 2011 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation is concerned with distributed decision making in networked multi-agent systems; that is, developing practical mechanisms which agents can utilize to autonomously coordinate their actions/decisions through local message exchanges and successfully achieve a system level goal with a satisfactory performance guarantee. In particular, this dissertation is divided into three parts and each one focuses on the following three classes of problems : (1) distributed average consensus; (2)distributed cooperative constrained optimization; (3) distributed online learning based coordination. This dissertation starts from the fundamental problem of distributed average consensus. In Part I, we first propose a class of dynamic average consensus algorithms and show that these algorithms allow agents to asymptotically track the average of a class of time-varying individual reference inputs. We then come up with a class of gossip-based algorithms which agents can use to achieve approximate average consensus via exchanging quantized information. Part II is concerned with a class of general multi-agent optimization problems. In particular, each agent is associated with a local objective function and a local constrained set. There is a pair of inequality and equality constraints known to all the agents. We first present a class of distributed primal-dual subgradient algorithms to solve the case when all the ingredients are convex. We then introduce a distributed approximate dual subgradient algorithm to address the non-convex counterpart. Part III studies distributed coordination schemes with online learning. The first problem considered is to optimally deploy a group of visual mobile sensors where the environmental distribution is unknown a priori. We formulate the problem as a non-cooperative game and come with up two distributed learning algorithms which allow sensors converge to the set of Nash equilibria and global optimum with probability one, respectively. The second problem is distributed formation control against a class of deception attacks. We propose a class of algorithms which allow vehicles adapt their strategies online and achieve the desired formation in the presence of deception attacks.
Book Synopsis Robust Multi-agent Decision Making in Faulty Environment by : International Business Machines Corporation. Research Division
Download or read book Robust Multi-agent Decision Making in Faulty Environment written by International Business Machines Corporation. Research Division and published by . This book was released on 1988 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Multi-Agent System Framework to Support the Decision-making in Complex Real-world Domains by : Thania Rendón Sallard
Download or read book A Multi-Agent System Framework to Support the Decision-making in Complex Real-world Domains written by Thania Rendón Sallard and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Algorithmic Decision-Making in Multi-Agent Systems: Votes and Prices by : Toni Böhnlein
Download or read book Algorithmic Decision-Making in Multi-Agent Systems: Votes and Prices written by Toni Böhnlein and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Uncertainty Handling and Decision Making in Multi-agent Cooperation by : Ping Xuan
Download or read book Uncertainty Handling and Decision Making in Multi-agent Cooperation written by Ping Xuan and published by . This book was released on 2002 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt:
