Layered Learning in Multiagent Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Layered Learning in Multiagent Systems write by Peter Stone. This book was released on 2000-03-03. Layered Learning in Multiagent Systems available in PDF, EPUB and Kindle. 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.
Layered Learning in Multi-Agent Systems
Layered Learning in Multi-Agent Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Layered Learning in Multi-Agent Systems write by Peter Stone. This book was released on 1998. Layered Learning in Multi-Agent Systems available in PDF, EPUB and Kindle. Multi-agent systems in complex, real-time domains require agents to act effectively both autonomously and as part of a team. This dissertation addresses multi-agent systems consisting of teams of autonomous agents acting in real-time, noisy, collaborative, and adversarial environments. Because of the inherent complexity of this type of multi-agent system, this thesis investigates the use of machine learning within multi-agent systems. The dissertation makes four main contributions to the fields of Machine Learning and Multi-Agent Systems. First, the thesis defines a team member agent architecture within which a flexible team structure is presented, allowing agents to decompose the task space into flexible roles and allowing them to smoothly switch roles while acting. Team organization is achieved by the introduction of a locker-room agreement as a collection of conventions followed by all team members. It defines agent roles, team formations, and pre-compiled multi-agent plans. In addition, the team member agent architecture includes a communication paradigm for domains with single-channel, low-bandwidth, unreliable communication. The communication paradigm facilitates team coordination while being robust to lost messages and active interference from opponents. Second, the thesis introduces layered learning, a general-purpose machine learning paradigm for complex domains in which learning a mapping directly from agents' sensors to their actuators is intractable. Given a hierarchical task decomposition, layered learning allows for learning at each level of the hierarchy, with learning at each level directly affecting learning at the next higher level. Third, the thesis introduces a new multi-agent reinforcement learning algorithm, namely team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL is designed for domains in which agents cannot necessarily observe the state changes when other team members act.
Layered Learning in Multi-Agent Systems
Layered Learning in Multi-Agent Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Layered Learning in Multi-Agent Systems write by Peter Stone. This book was released on 1998. Layered Learning in Multi-Agent Systems available in PDF, EPUB and Kindle. Multi-agent systems in complex, real-time domains require agents to act effectively both autonomously and as part of a team. This dissertation addresses multi-agent systems consisting of teams of autonomous agents acting in real-time, noisy, collaborative, and adversarial environments. Because of the inherent complexity of this type of multi-agent system, this thesis investigates the use of machine learning within multi-agent systems. The dissertation makes four main contributions to the fields of Machine Learning and Multi-Agent Systems. First, the thesis defines a team member agent architecture within which a flexible team structure is presented, allowing agents to decompose the task space into flexible roles and allowing them to smoothly switch roles while acting. Team organization is achieved by the introduction of a locker-room agreement as a collection of conventions followed by all team members. It defines agent roles, team formations, and pre-compiled multi-agent plans. In addition, the team member agent architecture includes a communication paradigm for domains with single-channel, low-bandwidth, unreliable communication. The communication paradigm facilitates team coordination while being robust to lost messages and active interference from opponents. Second, the thesis introduces layered learning, a general-purpose machine learning paradigm for complex domains in which learning a mapping directly from agents' sensors to their actuators is intractable. Given a hierarchical task decomposition, layered learning allows for learning at each level of the hierarchy, with learning at each level directly affecting learning at the next higher level. Third, the thesis introduces a new multi-agent reinforcement learning algorithm, namely team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL is designed for domains in which agents cannot necessarily observe the state changes when other team members act.
Multiagent Systems
Multiagent Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Multiagent Systems write by Gerhard Weiss. This book was released on 1999. Multiagent Systems available in PDF, EPUB and Kindle. An introduction to multiagent systems and contemporary distributed artificial intelligence, this text provides coverage of basic topics as well as closely-related ones. It emphasizes aspects of both theory and application and includes exercises of varying degrees of difficulty.
A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence write by Nikos Kolobov. This book was released on 2022-06-01. A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence available in PDF, EPUB and Kindle. 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.
