Scaling Multi Agent Learning In Complex Environments

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Scaling Multi-agent Learning in Complex Environments

Author : Chongjie Zhang
Publisher :
ISBN 13 :
Total Pages : 194 pages
Book Rating : 4.:/5 (786 download)

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Book Synopsis Scaling Multi-agent Learning in Complex Environments by : Chongjie Zhang

Download or read book Scaling Multi-agent Learning in Complex Environments written by Chongjie Zhang and published by . This book was released on 2011 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cooperative multi-agent systems (MAS) are finding applications in a wide variety of domains, including sensor networks, robotics, distributed control, collaborative decision support systems, and data mining. A cooperative MAS consists of a group of autonomous agents that interact with one another in order to optimize a global performance measure. A central challenge in cooperative MAS research is to design distributed coordination policies. Designing optimal distributed coordination policies offline is usually not feasible for large-scale complex multi-agent systems, where 10s to 1000s of agents are involved, there is limited communication bandwidth and communication delay between agents, agents have only limited partial views of the whole system, etc. This infeasibility is either due to a prohibitive cost to build an accurate decision model, or a dynamically evolving environment, or the intractable computation complexity. This thesis develops a multi-agent reinforcement learning paradigm to allow agents to effectively learn and adapt coordination policies in complex cooperative domains without explicitly building the complete decision models. With multi-agent reinforcement learning (MARL), agents explore the environment through trial and error, adapt their behaviors to the dynamics of the uncertain and evolving environment, and improve their performance through experiences. To achieve the scalability of MARL and ensure the global performance, the MARL paradigm developed in this thesis restricts the learning of each agent to using information locally observed or received from local interactions with a limited number of agents (i.e., neighbors) in the system and exploits non-local interaction information to coordinate the learning processes of agents. This thesis develops new MARL algorithms for agents to learn effectively with limited observations in multi-agent settings and introduces a low-overhead supervisory control framework to collect and integrate non-local information into the learning process of agents to coordinate their learning. More specifically, the contributions of already completed aspects of this thesis are as follows: Multi-Agent Learning with Policy Prediction: This thesis introduces the concept of policy prediction and augments the basic gradient-based learning algorithm to achieve two properties: best-response learning and convergence. The convergence property of multi-agent learning with policy prediction is proven for a class of static games under the assumption of full observability. MARL Algorithm with Limited Observability: This thesis develops PGA-APP, a practical multi-agent learning algorithm that extends Q-learning to learn stochastic policies. PGA-APP combines the policy gradient technique with the idea of policy prediction. It allows an agent to learn effectively with limited observability in complex domains in presence of other learning agents. The empirical results demonstrate that PGA-APP outperforms state-of-the-art MARL techniques in both benchmark games. MARL Application in Cloud Computing: This thesis illustrates how MARL can be applied to optimizing online distributed resource allocation in cloud computing. Empirical results show that the MARL approach performs reasonably well, compared to an optimal solution, and better than a centralized myopic allocation approach in some cases. A General Paradigm for Coordinating MARL: This thesis presents a multi-level supervisory control framework to coordinate and guide the agents' learning process. This framework exploits non-local information and introduces a more global view to coordinate the learning process of individual agents without incurring significant overhead and exploding their policy space. Empirical results demonstrate that this coordination significantly improves the speed, quality and likelihood of MARL convergence in large-scale, complex cooperative multi-agent systems. An Agent Interaction Model: This thesis proposes a new general agent interaction model. This interaction model formalizes a type of interactions among agents, called {\em joint-even-driven} interactions, and define a measure for capturing the strength of such interactions. Formal analysis reveals the relationship between interactions between agents and the performance of individual agents and the whole system. Self-Organization for Nearly-Decomposable Hierarchy: This thesis develops a distributed self-organization approach, based on the agent interaction model, that dynamically form a nearly decomposable hierarchy for large-scale multi-agent systems. This self-organization approach is integrated into supervisory control framework to automatically evolving supervisory organizations to better coordinating MARL during the learning process. Empirically results show that dynamically evolving supervisory organizations can perform better than static ones. Automating Coordination for Multi-Agent Learning: We tailor our supervision framework for coordinating MARL in ND-POMDPs. By exploiting structured interaction in ND-POMDPs, this tailored approach distributes the learning of the global joint policy among supervisors and employs DCOP techniques to automatically coordinate distributed learning to ensure the global learning performance. We prove that this approach can learn a globally optimal policy for ND-POMDPs with a property called groupwise observability.

Transfer Learning for Multiagent Reinforcement Learning Systems

Author : Felipe Leno da Silva
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1636391354
Total Pages : 131 pages
Book Rating : 4.6/5 (363 download)

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Book Synopsis Transfer Learning for Multiagent Reinforcement Learning Systems by : Felipe Leno da Silva

Download or read book Transfer Learning for Multiagent Reinforcement Learning Systems written by Felipe Leno da Silva and published by Morgan & Claypool Publishers. This book was released on 2021-05-27 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning to solve sequential decision-making tasks is difficult. Humans take years exploring the environment essentially in a random way until they are able to reason, solve difficult tasks, and collaborate with other humans towards a common goal. Artificial Intelligent agents are like humans in this aspect. Reinforcement Learning (RL) is a well-known technique to train autonomous agents through interactions with the environment. Unfortunately, the learning process has a high sample complexity to infer an effective actuation policy, especially when multiple agents are simultaneously actuating in the environment. However, previous knowledge can be leveraged to accelerate learning and enable solving harder tasks. In the same way humans build skills and reuse them by relating different tasks, RL agents might reuse knowledge from previously solved tasks and from the exchange of knowledge with other agents in the environment. In fact, virtually all of the most challenging tasks currently solved by RL rely on embedded knowledge reuse techniques, such as Imitation Learning, Learning from Demonstration, and Curriculum Learning. This book surveys the literature on knowledge reuse in multiagent RL. The authors define a unifying taxonomy of state-of-the-art solutions for reusing knowledge, providing a comprehensive discussion of recent progress in the area. In this book, readers will find a comprehensive discussion of the many ways in which knowledge can be reused in multiagent sequential decision-making tasks, as well as in which scenarios each of the approaches is more efficient. The authors also provide their view of the current low-hanging fruit developments of the area, as well as the still-open big questions that could result in breakthrough developments. Finally, the book provides resources to researchers who intend to join this area or leverage those techniques, including a list of conferences, journals, and implementation tools. This book will be useful for a wide audience; and will hopefully promote new dialogues across communities and novel developments in the area.

Coordination of Large-Scale Multiagent Systems

Author : Paul Scerri
Publisher : Springer Science & Business Media
ISBN 13 : 0387279725
Total Pages : 343 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis Coordination of Large-Scale Multiagent Systems by : Paul Scerri

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 2006-03-14 with total page 343 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.

Scaling Reinforcement Learning to the Unconstrained Multi-agent Domain

Author : Victor Palmer
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (61 download)

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Book Synopsis Scaling Reinforcement Learning to the Unconstrained Multi-agent Domain by : Victor Palmer

Download or read book Scaling Reinforcement Learning to the Unconstrained Multi-agent Domain written by Victor Palmer and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning is a machine learning technique designed to mimic the way animals learn by receiving rewards and punishment. It is designed to train intelligent agents when very little is known about the agent's environment, and consequently the agent's designer is unable to hand-craft an appropriate policy. Using reinforcement learning, the agent's designer can merely give reward to the agent when it does something right, and the algorithm will craft an appropriate policy automatically. In many situations it is desirable to use this technique to train systems of agents (for example, to train robots to play RoboCup soccer in a coordinated fashion). Unfortunately, several significant computational issues occur when using this technique to train systems of agents. This dissertation introduces a suite of techniques that overcome many of these difficulties in various common situations. First, we show how multi-agent reinforcement learning can be made more tractable by forming coalitions out of the agents, and training each coalition separately. Coalitions are formed by using information-theoretic techniques, and we find that by using a coalition-based approach, the computational complexity of reinforcement-learning can be made linear in the total system agent count. Next we look at ways to integrate domain knowledge into the reinforcement learning process, and how this can significantly improve the policy quality in multi-agent situations. Specifically, we find that integrating domain knowledge into a reinforcement learning process can overcome training data deficiencies and allow the learner to converge to acceptable solutions when lack of training data would have prevented such convergence without domain knowledge. We then show how to train policies over continuous action spaces, which can reduce problem complexity for domains that require continuous action spaces (analog controllers) by eliminating the need to finely discretize the action space. Finally, we look at ways to perform reinforcement learning on modern GPUs and show how by doing this we can tackle significantly larger problems. We find that by offloading some of the RL computation to the GPU, we can achieve almost a 4.5 speedup factor in the total training process.

Distributed Optimization-Based Control of Multi-Agent Networks in Complex Environments

Author : Minghui Zhu
Publisher : Springer
ISBN 13 : 3319190725
Total Pages : 133 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Distributed Optimization-Based Control of Multi-Agent Networks in Complex Environments by : Minghui Zhu

Download or read book Distributed Optimization-Based Control of Multi-Agent Networks in Complex Environments written by Minghui Zhu and published by Springer. This book was released on 2015-06-11 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Cooperation and Communication in Multiagent Deep Reinforcement Learning

Author : Matthew John Hausknecht
Publisher :
ISBN 13 :
Total Pages : 318 pages
Book Rating : 4.:/5 (972 download)

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Book Synopsis Cooperation and Communication in Multiagent Deep Reinforcement Learning by : Matthew John Hausknecht

Download or read book Cooperation and Communication in Multiagent Deep Reinforcement Learning written by Matthew John Hausknecht and published by . This book was released on 2017 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning is the area of machine learning concerned with learning which actions to execute in an unknown environment in order to maximize cumulative reward. As agents begin to perform tasks of genuine interest to humans, they will be faced with environments too complex for humans to predetermine the correct actions using hand-designed solutions. Instead, capable learning agents will be necessary to tackle complex real-world domains. However, traditional reinforcement learning algorithms have difficulty with domains featuring 1) high-dimensional continuous state spaces, for example pixels from a camera image, 2) high-dimensional parameterized-continuous action spaces, 3) partial observability, and 4) multiple independent learning agents. We hypothesize that deep neural networks hold the key to scaling reinforcement learning towards complex tasks. This thesis seeks to answer the following two-part question: 1) How can the power of Deep Neural Networks be leveraged to extend Reinforcement Learning to complex environments featuring partial observability, high-dimensional parameterized-continuous state and action spaces, and sparse rewards? 2) How can multiple Deep Reinforcement Learning agents learn to cooperate in a multiagent setting? To address the first part of this question, this thesis explores the idea of using recurrent neural networks to combat partial observability experienced by agents in the domain of Atari 2600 video games. Next, we design a deep reinforcement learning agent capable of discovering effective policies for the parameterized-continuous action space found in the Half Field Offense simulated soccer domain. To address the second part of this question, this thesis investigates architectures and algorithms suited for cooperative multiagent learning. We demonstrate that sharing parameters and memories between deep reinforcement learning agents fosters policy similarity, which can result in cooperative behavior. Additionally, we hypothesize that communication can further aid cooperation, and we present the Grounded Semantic Network (GSN), which learns a communication protocol grounded in the observation space and reward function of the task. In general, we find that the GSN is effective on domains featuring partial observability and asymmetric information. All in all, this thesis demonstrates that reinforcement learning combined with deep neural network function approximation can produce algorithms capable of discovering effective policies for domains with partial observability, parameterized-continuous actions spaces, and sparse rewards. Additionally, we demonstrate that single agent deep reinforcement learning algorithms can be naturally extended towards cooperative multiagent tasks featuring learned communication. These results represent a non-trivial step towards extending agent-based AI towards complex environments.

Proceedings of the Sixth International Conference on Computer Supported Cooperative Work in Design

Author : Shen Weiming
Publisher : NRC Research Press
ISBN 13 : 9780660184937
Total Pages : 604 pages
Book Rating : 4.1/5 (849 download)

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Book Synopsis Proceedings of the Sixth International Conference on Computer Supported Cooperative Work in Design by : Shen Weiming

Download or read book Proceedings of the Sixth International Conference on Computer Supported Cooperative Work in Design written by Shen Weiming and published by NRC Research Press. This book was released on 2001 with total page 604 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer-supported co-operative work (CSCW) is a research area that aims at integrating the works of several people involved in a common goal, inside a co-operative universe, through the sharing of resources in an efficient way. This report contains the papers presented at a conference on CSCW in design. Topics covered include: techniques, methods, and tools for CSCW in design; social organization of the CSCW process; integration of methods & tools within the work organization; co-operation in virtual enterprises and electronic businesses; CSCW in design & manufacturing; interaction between the CSCW approach and knowledge reuse as found in knowledge management; intelligent agent & multi-agent systems; Internet/World Wide Web and CSCW in design; and applications & test beds.

Advanced Machine Learning Approaches in Cancer Prognosis

Author : Janmenjoy Nayak
Publisher : Springer Nature
ISBN 13 : 3030719758
Total Pages : 461 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Advanced Machine Learning Approaches in Cancer Prognosis by : Janmenjoy Nayak

Download or read book Advanced Machine Learning Approaches in Cancer Prognosis written by Janmenjoy Nayak and published by Springer Nature. This book was released on 2021-05-29 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces a variety of advanced machine learning approaches covering the areas of neural networks, fuzzy logic, and hybrid intelligent systems for the determination and diagnosis of cancer. Moreover, the tactical solutions of machine learning have proved its vast range of significance and, provided novel solutions in the medical field for the diagnosis of disease. This book also explores the distinct deep learning approaches that are capable of yielding more accurate outcomes for the diagnosis of cancer. In addition to providing an overview of the emerging machine and deep learning approaches, it also enlightens an insight on how to evaluate the efficiency and appropriateness of such techniques and analysis of cancer data used in the cancer diagnosis. Therefore, this book focuses on the recent advancements in the machine learning and deep learning approaches used in the diagnosis of different types of cancer along with their research challenges and future directions for the targeted audience including scientists, experts, Ph.D. students, postdocs, and anyone interested in the subjects discussed.

Deep Multi Agent Reinforcement Learning for Autonomous Driving

Author : Sushrut Bhalla
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (126 download)

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Book Synopsis Deep Multi Agent Reinforcement Learning for Autonomous Driving by : Sushrut Bhalla

Download or read book Deep Multi Agent Reinforcement Learning for Autonomous Driving written by Sushrut Bhalla and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning and back-propagation have been successfully used to perform centralized training with communication protocols among multiple agents in a cooperative Multi-Agent Deep Reinforcement Learning (MARL) environment. In this work, I present techniques for centralized training of MARL agents in large scale environments and compare my work against current state of the art techniques. This work uses model-free Deep Q-Network (DQN) as the baseline model and allows inter agent communication for cooperative policy learning. I present two novel, scalable and centralized MARL training techniques (MA-MeSN, MA-BoN), which are developed under the principle that the behavior policy and message/communication policies have different optimization criteria. Thus, this work presents models which separate the message learning module from the behavior policy learning module. As shown in the experiments, the separation of these modules helps in faster convergence in complex domains like autonomous driving simulators and achieves better results than the current techniques in literature. Subsequently, this work presents two novel techniques for achieving decentralized execution for the communication based cooperative policy. The first technique uses behavior cloning as a method of cloning an expert cooperative policy to a decentralized agent without message sharing. In the second method, the behavior policy is coupled with a memory module which is local to each model. This memory model is used by the independent agents to mimic the communication policies of other agents and thus generate an independent behavior policy. This decentralized approach has minimal effect on degradation of the overall cumulative reward achieved by the centralized policy. Using a fully decentralized approach allows us to address the challenges of noise and communication bottlenecks in real-time communication channels. In this work, I theoretically and empirically compare the centralized and decentralized training algorithms to current research in the field of MARL. As part of this thesis, I also developed a large scale multi-agent testing environment. It is a new OpenAI-Gym environment which can be used for large scale multi-agent research as it simulates multiple autonomous cars driving cooperatively on a highway in the presence of a bad actor. I compare the performance of the centralized algorithms to existing state-of-the-art algorithms, for ex, DIAL and IMS which are based on cumulative reward achieved per episode and other metrics. MA-MeSN and MA-BoN achieve a cumulative reward of at-least 263% higher than the reward achieved by the DIAL and IMS. I also present an ablation study of the scalability of MA-BoN and show that MA-MeSN and MA-BoN algorithms only exhibit a linear increase in inference time and number of trainable parameters compared to quadratic increase for DIAL.

ECAI 2023

Author : K. Gal
Publisher : IOS Press
ISBN 13 : 164368437X
Total Pages : 3328 pages
Book Rating : 4.6/5 (436 download)

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Book Synopsis ECAI 2023 by : K. Gal

Download or read book ECAI 2023 written by K. Gal and published by IOS Press. This book was released on 2023-10-18 with total page 3328 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021)

Author : Meiping Wu
Publisher : Springer Nature
ISBN 13 : 9811694923
Total Pages : 3575 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021) by : Meiping Wu

Download or read book Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021) written by Meiping Wu and published by Springer Nature. This book was released on 2022-03-18 with total page 3575 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes original, peer-reviewed research papers from the ICAUS 2021, which offers a unique and interesting platform for scientists, engineers and practitioners throughout the world to present and share their most recent research and innovative ideas. The aim of the ICAUS 2021 is to stimulate researchers active in the areas pertinent to intelligent unmanned systems. The topics covered include but are not limited to Unmanned Aerial/Ground/Surface/Underwater Systems, Robotic, Autonomous Control/Navigation and Positioning/ Architecture, Energy and Task Planning and Effectiveness Evaluation Technologies, Artificial Intelligence Algorithm/Bionic Technology and Its Application in Unmanned Systems. The papers showcased here share the latest findings on Unmanned Systems, Robotics, Automation, Intelligent Systems, Control Systems, Integrated Networks, Modeling and Simulation. It makes the book a valuable asset for researchers, engineers, and university students alike.

Scaling Multiagent Reinforcement Learning

Author : Scott Proper
Publisher :
ISBN 13 :
Total Pages : 246 pages
Book Rating : 4.:/5 (491 download)

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Book Synopsis Scaling Multiagent Reinforcement Learning by : Scott Proper

Download or read book Scaling Multiagent Reinforcement Learning written by Scott Proper and published by . This book was released on 2010 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning in real-world domains suffers from three curses of dimensionality: explosions in state and action spaces, and high stochasticity or "outcome space" explosion. Multiagent domains are particularly susceptible to these problems. This thesis describes ways to mitigate these curses in several different multiagent domains, including real-time delivery of products using multiple vehicles with stochastic demands, a multiagent predator-prey domain, and a domain based on a real-time strategy game. To mitigate the problem of state-space explosion, this thesis present several approaches that mitigate each of these curses. "Tabular linear functions" (TLFs) are introduced that generalize tile-coding and linear value functions and allow learning of complex nonlinear functions in high-dimensional state-spaces. It is also shown how to adapt TLFs to relational domains, creating a "lifted" version called relational templates. To mitigate the problem of action-space explosion, the replacement of complete joint action space search with a form of hill climbing is described. To mitigate the problem of outcome space explosion, a more efficient calculation of the expected value of the next state is shown, and two real-time dynamic programming algorithms based on afterstates, ASH-learning and ATR-learning, are introduced. Lastly, two approaches that scale by treating a multiagent domain as being formed of several coordinating agents are presented. "Multiagent H-learning" and "Multiagent ASH-learning" are described, where coordination is achieved through a method called "serial coordination". This technique has the benefit of addressing each of the three curses of dimensionality simultaneously by reducing the space of states and actions each local agent must consider. The second approach to multiagent coordination presented is "assignment-based decomposition", which divides the action selection step into an assignment phase and a primitive action selection step. Like the multiagent approach, assignment-based decomposition addresses all three curses of dimensionality simultaneously by reducing the space of states and actions each group of agents must consider. This method is capable of much more sophisticated coordination. Experimental results are presented which show successful application of all methods described. These results demonstrate that the scaling techniques described in this thesis can greatly mitigate the three curses of dimensionality and allow solutions for multiagent domains to scale to large numbers of agents, and complex state and outcome spaces.

Layered Learning in Multiagent Systems

Author : Peter Stone
Publisher : MIT Press
ISBN 13 : 9780262264600
Total Pages : 300 pages
Book Rating : 4.2/5 (646 download)

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Book Synopsis Layered Learning in Multiagent Systems by : Peter Stone

Download or read book Layered Learning in Multiagent Systems written by Peter Stone and published by MIT Press. This book was released on 2000-03-03 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book looks at multiagent systems that consist of teams of autonomous agents acting in real-time, noisy, collaborative, and adversarial environments. This book looks at multiagent systems that consist of teams of autonomous agents acting in real-time, noisy, collaborative, and adversarial environments. The book makes four main contributions to the fields of machine learning and multiagent systems. First, it describes an architecture within which a flexible team structure allows member agents to decompose a task into flexible roles and to switch roles while acting. Second, it presents layered learning, a general-purpose machine-learning method for complex domains in which learning a mapping directly from agents' sensors to their actuators is intractable with existing machine-learning methods. Third, the book introduces a new multiagent reinforcement learning algorithm—team-partitioned, opaque-transition reinforcement learning (TPOT-RL)—designed for domains in which agents cannot necessarily observe the state-changes caused by other agents' actions. The final contribution is a fully functioning multiagent system that incorporates learning in a real-time, noisy domain with teammates and adversaries—a computer-simulated robotic soccer team. Peter Stone's work is the basis for the CMUnited Robotic Soccer Team, which has dominated recent RoboCup competitions. RoboCup not only helps roboticists to prove their theories in a realistic situation, but has drawn considerable public and professional attention to the field of intelligent robotics. The CMUnited team won the 1999 Stockholm simulator competition, outscoring its opponents by the rather impressive cumulative score of 110-0.

Massively Multi-Agent Systems II

Author : Donghui Lin
Publisher : Springer
ISBN 13 : 3030209377
Total Pages : 168 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Massively Multi-Agent Systems II by : Donghui Lin

Download or read book Massively Multi-Agent Systems II written by Donghui Lin and published by Springer. This book was released on 2019-05-18 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains revised selected and invited papers presented at the International Workshop on Massively Multi-Agent Systems, MMAS 2018, held in Stockholm, Sweden, in July 2018. The 7 revised full papers presented were carefully reviewed and selected for inclusion in this volume. Also included are 3 post-workshop papers. The papers discuss enabling technologies, new architectures, promising applications, and challenges of massively multi-agent systems in the era of IoT. They are organized in the following topical sections: multi-agent systems and Internet of Things; architectures for massively multi-agent systems; and applications of massively multi-agent systems.

7th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS'09)

Author : Yves Demazeau
Publisher : Springer Science & Business Media
ISBN 13 : 3642004873
Total Pages : 603 pages
Book Rating : 4.6/5 (42 download)

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Book Synopsis 7th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS'09) by : Yves Demazeau

Download or read book 7th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS'09) written by Yves Demazeau and published by Springer Science & Business Media. This book was released on 2009-03-08 with total page 603 pages. Available in PDF, EPUB and Kindle. Book excerpt: PAAMS, the International Conference on Practical Applications of Agents and Multi-Agent Systems is an evolution of the International Workshop on Practical Applications of Agents and Multi-Agent Systems. PAAMS is an international yearly tribune to present, to discuss, and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to exchange their experience in the development of Agents and Multi-Agent Systems. This volume presents the papers that have been accepted for the 2009 edition. These articles capture the most innovative results and this year’s trends: Assisted Cognition, E-Commerce, Grid Computing, Human Modelling, Information Systems, Knowledge Management, Agent-Based Simulation, Software Development, Transports, Trust and Security. Each paper has been reviewed by three different reviewers, from an international committee composed of 64 members from 20 different countries. From the 92 submissions received, 35 were selected for full presentation at the conference, and 26 were accepted as posters.

Multi-Agent Coordination

Author : Arup Kumar Sadhu
Publisher : John Wiley & Sons
ISBN 13 : 1119699029
Total Pages : 320 pages
Book Rating : 4.1/5 (196 download)

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Book Synopsis Multi-Agent Coordination by : Arup Kumar Sadhu

Download or read book Multi-Agent Coordination written by Arup Kumar Sadhu and published by John Wiley & Sons. This book was released on 2020-12-01 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the latest developments in multi-robot coordination techniques with this insightful and original resource Multi-Agent Coordination: A Reinforcement Learning Approach delivers a comprehensive, insightful, and unique treatment of the development of multi-robot coordination algorithms with minimal computational burden and reduced storage requirements when compared to traditional algorithms. The accomplished academics, engineers, and authors provide readers with both a high-level introduction to, and overview of, multi-robot coordination, and in-depth analyses of learning-based planning algorithms. You'll learn about how to accelerate the exploration of the team-goal and alternative approaches to speeding up the convergence of TMAQL by identifying the preferred joint action for the team. The authors also propose novel approaches to consensus Q-learning that address the equilibrium selection problem and a new way of evaluating the threshold value for uniting empires without imposing any significant computation overhead. Finally, the book concludes with an examination of the likely direction of future research in this rapidly developing field. Readers will discover cutting-edge techniques for multi-agent coordination, including: An introduction to multi-agent coordination by reinforcement learning and evolutionary algorithms, including topics like the Nash equilibrium and correlated equilibrium Improving convergence speed of multi-agent Q-learning for cooperative task planning Consensus Q-learning for multi-agent cooperative planning The efficient computing of correlated equilibrium for cooperative q-learning based multi-agent planning A modified imperialist competitive algorithm for multi-agent stick-carrying applications Perfect for academics, engineers, and professionals who regularly work with multi-agent learning algorithms, Multi-Agent Coordination: A Reinforcement Learning Approach also belongs on the bookshelves of anyone with an advanced interest in machine learning and artificial intelligence as it applies to the field of cooperative or competitive robotics.

Multi-agent and Complex Systems

Author : Quan Bai
Publisher : Springer
ISBN 13 : 9811025649
Total Pages : 210 pages
Book Rating : 4.8/5 (11 download)

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Book Synopsis Multi-agent and Complex Systems by : Quan Bai

Download or read book Multi-agent and Complex Systems written by Quan Bai and published by Springer. This book was released on 2016-11-15 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a description of advanced multi-agent and artificial intelligence technologies for the modeling and simulation of complex systems, as well as an overview of the latest scientific efforts in this field. A complex system features a large number of interacting components, whose aggregate activities are nonlinear and self-organized. A multi-agent system is a group or society of agents which interact with others cooperatively and/or competitively in order to reach their individual or common goals. Multi-agent systems are suitable for modeling and simulation of complex systems, which is difficult to accomplish using traditional computational approaches.