Multiagent Robotic Systems

Addresses learning and adaptation in decentralized autonomous robots. This title presents a systematic examination that demonstrates the interrelationships between the autonomy of individual robots and the emerged global behavior properties of a group performing a cooperative task.

Providing a guided tour of the pioneering work and major technical issues, Multiagent Robotic Systems addresses learning and adaptation in decentralized autonomous robots. Its systematic examination demonstrates the interrelationships between the autonomy of individual robots and the emerged global behavior properties of a group performing a cooperative task. The author also includes descriptions of the essential building blocks of the architecture of autonomous mobile robots with respect to their requirement on local behavioral conditioning and group behavioral evolution.
After reading this book you will be able to fully appreciate the strengths and usefulness of various approaches in the development and application of multiagent robotic systems. It covers:

Why and how to develop and experimentally test the computational mechanisms for learning and evolving sensory-motor control behaviors in autonomous robots

How to design and develop evolutionary algorithm-based group behavioral learning mechanisms for the optimal emergence of group behaviors

How to enable group robots to converge to a finite number of desirable task states through group learning

What are the effects of the local learning mechanisms on the emergent global behaviors

How to use decentralized, self-organizing autonomous robots to perform cooperative tasks in an unknown environment
Earlier works have focused primarily on how to navigate in a spatially unknown environment, given certain predefined motion behaviors. What is missing, however, is an in-depth look at the important issues on how to effectively obtain such behaviors in group robots and how to enable behavioral learning and adaptation at the group level. Multiagent Robotic Systems examines the key methodological issues and gives you an understanding of the underlying computational models and techniques for multiagent systems.

Why and how to develop and experimentally test the computational mechanisms for learning and evolving sensory-motor control behaviors in autonomous robots

How to design and develop evolutionary algorithm-based group behavioral learning mechanisms for the optimal emergence of group behaviors

How to enable group robots to converge to a finite number of desirable task states through group learning

What are the effects of the local learning mechanisms on the emergent global behaviors

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