Layered Learning in Multi-Agent Systems

Released on by Peter Stone
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  • Layered Learning in Multi-Agent Systems Book Detail

  • Author : Peter Stone
  • Release Date : 1998
  • Publisher :
  • Genre : Intelligent agents (Computer software)
  • Pages : 247
  • ISBN 13 :
  • File Size : 47,47 MB
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Layered Learning in Multi-Agent Systems by Peter Stone PDF Summary

Book Description: 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.

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Layered Learning in Multi-Agent Systems

Layered Learning in Multi-Agent Systems

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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-a

Layered Learning in Multi-Agent Systems

Layered Learning in Multi-Agent Systems

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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-a

Layered Learning in Multiagent Systems

Layered Learning in Multiagent Systems

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This book looks at multiagent systems that consist of teams of autonomous agents acting in real-time, noisy, collaborative, and adversarial environments. This b

Multi-Agent Systems and Applications III

Multi-Agent Systems and Applications III

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This book constitutes the refereed proceedings of the International Central and European Conference on Multi-Agent Systems, CEEMAS 2003, held in Prague, Czech R