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Author: Scott T. Sosnowski Publisher: ISBN: Category : Artificial intelligence Languages : en Pages : 0
Book Description
Many practical decision-making problems involve coordinating teams of agents. In our work, we focus on the problem of coordinated action selection in reinforcement learning with large stochastic multi-agent systems that are centrally controlled. Previous work has shown how to formulate coordination as exact inference in a Markov network, but this becomes intractable for large teams of agents. We investigate the idea of "approximate coordination" as a solution to an approximate inference problem in a Markov network. We look at a pursuit domain and a simplified real-time strategy game and find that in these situations, such approaches are able to find good policies when exact approaches become intractable.
Author: Scott T. Sosnowski Publisher: ISBN: Category : Artificial intelligence Languages : en Pages : 0
Book Description
Many practical decision-making problems involve coordinating teams of agents. In our work, we focus on the problem of coordinated action selection in reinforcement learning with large stochastic multi-agent systems that are centrally controlled. Previous work has shown how to formulate coordination as exact inference in a Markov network, but this becomes intractable for large teams of agents. We investigate the idea of "approximate coordination" as a solution to an approximate inference problem in a Markov network. We look at a pursuit domain and a simplified real-time strategy game and find that in these situations, such approaches are able to find good policies when exact approaches become intractable.
Author: Dimitri Bertsekas Publisher: Athena Scientific ISBN: 1886529078 Category : Computers Languages : en Pages : 498
Book Description
The purpose of this book is to develop in greater depth some of the methods from the author's Reinforcement Learning and Optimal Control recently published textbook (Athena Scientific, 2019). In particular, we present new research, relating to systems involving multiple agents, partitioned architectures, and distributed asynchronous computation. We pay special attention to the contexts of dynamic programming/policy iteration and control theory/model predictive control. We also discuss in some detail the application of the methodology to challenging discrete/combinatorial optimization problems, such as routing, scheduling, assignment, and mixed integer programming, including the use of neural network approximations within these contexts. The book focuses on the fundamental idea of policy iteration, i.e., start from some policy, and successively generate one or more improved policies. If just one improved policy is generated, this is called rollout, which, based on broad and consistent computational experience, appears to be one of the most versatile and reliable of all reinforcement learning methods. In this book, rollout algorithms are developed for both discrete deterministic and stochastic DP problems, and the development of distributed implementations in both multiagent and multiprocessor settings, aiming to take advantage of parallelism. Approximate policy iteration is more ambitious than rollout, but it is a strictly off-line method, and it is generally far more computationally intensive. This motivates the use of parallel and distributed computation. One of the purposes of the monograph is to discuss distributed (possibly asynchronous) methods that relate to rollout and policy iteration, both in the context of an exact and an approximate implementation involving neural networks or other approximation architectures. Much of the new research is inspired by the remarkable AlphaZero chess program, where policy iteration, value and policy networks, approximate lookahead minimization, and parallel computation all play an important role.
Author: Paul Scerri Publisher: Springer Science & Business Media ISBN: 9780387261935 Category : Computers Languages : en Pages : 366
Book Description
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.
Author: Paul Scerri Publisher: Springer Science & Business Media ISBN: 0387279725 Category : Computers Languages : en Pages : 343
Book Description
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.
Author: Chris HolmesParker Publisher: ISBN: Category : Multiagent systems Languages : en Pages : 143
Book Description
Recent advances in multiagent learning have led to exciting new capabilities spanning fields as diverse as planetary exploration, air traffic control, military reconnaissance, and airport security. Such algorithms provide a tangible benefit over traditional control algorithms in that they allow fast responses, adapt to dynamic environments, and generally scale well. Unfortunately, because many existing multiagent learning methods are extensions of single agent approaches, they are inhibited by three key issues: i) they treat the actions of other agents as "environmental noise" in an attempt to simplify the problem complexity, ii) they are slow to converge in large systems as the joint action space grows exponentially in the number of agents, and iii) they frequently rely upon the presence of an accurate system model being readily available. This work addresses these three issues sequentially. First, we improve overall learning performance compared to existing state-of-the-art techniques in the field by embracing the exploration in learning rather than ignoring it or approximating it away. Within multiagent systems, exploration by individual agents significantly alters the dynamics of the environment in which all agents learn. To address this, we introduce the concept of "private" exploration, which enables each agent to present a stationary baseline policy to other agents in order to allow other agents in the system to learn more efficiently. In particular, we introduce Coordinated Learning without Exploratory Action Noise (CLEAN) rewards which improve coordination and performance by utilizing the concept of private exploration in order to remove the negative impact of traditional "public" exploration strategies from learning in multiagent systems. Next, we leverage the fundamental properties of CLEAN rewards that enable private exploration to allow agents to explore multiple potential actions concurrently in a "batch mode" in order to significantly improve learning speed over the state-of-the-art. Finally, we improve the real-world applicability of the proposed techniques by reducing their requirements. Specifically, the CLEAN rewards developed require an accurate partial model (i.e., an accurate model of the system objective) of the system in order to be computed. Unfortunately, many real-world systems are too complex to be modeled or are not known in advance, so an accurate system model is not available a priori. We address this shortcoming by employing model-based reinforcement learning techniques to enable agents to construct their own approximate model of the system objective based upon their observations and use this approximate model to calculate their CLEAN rewards.
Author: Nikos Kolobov Publisher: Springer Nature ISBN: 3031015436 Category : Computers Languages : en Pages : 71
Book Description
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.
Author: Sven Koenig Publisher: Springer Science & Business Media ISBN: 3540439412 Category : Computers Languages : en Pages : 360
Book Description
It has been recognized since the inception of Artificial Intelligence (AI) that abstractions, problem reformulations, and approximations (AR&A) are central to human common sense reasoning and problem solving and to the ability of systems to reason effectively in complex domains. AR&A techniques have been used to solve a variety of tasks, including automatic programming, constraint satisfaction, design, diagnosis, machine learning, search, planning, reasoning, game playing, scheduling, and theorem proving. The primary purpose of AR&A techniques in such settings is to overcome computational intractability. In addition, AR&A techniques are useful for accelerating learning and for summarizing sets of solutions. This volume contains the proceedings of SARA 2002, the fifth Symposium on Abstraction, Reformulation, and Approximation, held at Kananaskis Mountain Lodge, Kananaskis Village, Alberta (Canada), August 2 4, 2002. The SARA series is the continuation of two separate threads of workshops: AAAI workshops in 1990 and 1992, and an ad hoc series beginning with the "Knowledge Compilation" workshop in 1986 and the "Change of Representation and Inductive Bias" workshop in 1988 with followup workshops in 1990 and 1992. The two workshop series merged in 1994 to form the first SARA. Subsequent SARAs were held in 1995, 1998, and 2000.
Author: Thomas A. Wagner Publisher: Springer Science & Business Media ISBN: 1402078684 Category : Computers Languages : en Pages : 251
Book Description
An Application Science For Multi-Agent Systems addresses the complexity of choosing which multi-agent control technologies are appropriate for a given problem domain or a given application. Without such knowledge, when faced with a new application domain, agent developers must rely on past experience and intuition to determine whether a multi-agent system is the right approach, and if so, how to structure the agents, how to decompose the problem, and how to coordinate the activities of the agents, and so forth. This unique collection of contributions, written by leading international researchers in the agent community, provides valuable insight into the issues of deciding which technique to apply and when it is appropriate to use them. The contributions also discuss potential trade-offs or caveats involved with each decision. An Application Science For Multi-Agent Systems is an excellent reference for anyone involved in developing multi-agent systems.
Author: Ralph Bergmann Publisher: Springer ISBN: 354087805X Category : Computers Languages : en Pages : 217
Book Description
For the sixth time, the German special interest group on Distributed Arti?cial Intelligence in cooperation with the Steering Committee of MATES organized the German Conference on Multiagent System Technologies – MATES 2008. This conference, which took place during September 23–26, 2008 in Kaisersla- ern, followed a series of successful predecessor conferences in Erfurt (2003, 2004, and 2006), Koblenz (2005), and Leipzig (2007). MATES 2008 was co-located with the 31st German Conference on Arti?cial Intelligence (KI 2008) and was hosted by the University of Kaiserslautern and the German Research Center for Arti?cial Intelligence (DFKI). As in recent years, MATES 2008 provided a distinguished, lively, and - terdisciplinary forum for researchers, users, and developers of agent technology to present and discuss the latest advances of research and development in the area of autonomous agents and multiagent systems. Accordingly, the topics of MATES 2008 covered the whole range: from the theory to applications of agent and multiagent technology. In all, 35 papers were submitted from authors from 11 countries. The accepted 16 full papers included in this proceedings volume and presented as talks at the conference were chosen based on a thorough and highly selective review process. Each paper was reviewed and discussed by at least three Program Committee members and revised according to their c- ments. We believe that the papers of this volume are a representative snapshot of current research and contribute to both theoretical and applied aspects of autonomous agents and multiagent systems.
Author: Joshua David Redding Publisher: ISBN: Category : Languages : en Pages : 131
Book Description
Teams of autonomous mobile robotic agents will play an important role in the future of robotics. Efficient coordination of these agents within large, cooperative teams is an important characteristic of any system utilizing multiple autonomous vehicles. Applications of such a cooperative technology stretch beyond multi-robot systems to include satellite formations, networked systems, traffic flow, and many others. The diversity of capabilities offered by a team, as opposed to an individual, has attracted the attention of both researchers and practitioners in part due to the associated challenges such as the combinatorial nature of joint action selection among interdependent agents. This thesis aims to address the issues of the issues of scalability and adaptability within teams of such inter-dependent agents while planning, coordinating, and learning in a decentralized environment. In doing so, the first focus is the integration of learning and adaptation algorithms into a multi-agent planning architecture to enable online adaptation of planner parameters. A second focus is the development of approximation algorithms to reduce the computational complexity of decentralized multi-agent planning methods. Such a reduction improves problem scalability and ultimately enables much larger robot teams. Finally, we are interested in implementing these algorithms in meaningful, real-world scenarios. As robots and unmanned systems continue to advance technologically, enabling a self-awareness as to their physical state of health will become critical. In this context, the architecture and algorithms developed in this thesis are implemented in both hardware and software flight experiments under a class of cooperative multi-agent systems we call persistent health management scenarios.
Author: Scott T. Sosnowski
Author: Scott T. Sosnowski
Author: Dimitri Bertsekas
Author: Paul Scerri
Author: Paul Scerri
Author: Chris HolmesParker
Author: Nikos Kolobov
Author: Sven Koenig
Author: Thomas A. Wagner
Author: Ralph Bergmann
Author: Joshua David Redding