By Jiming Liu

ISBN-10: 1420038834

ISBN-13: 9781420038835

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Floreano and Urzelai [FU00] have identified at least three approaches to evolutionary robotics: automated engineering, artificial life, and synthetic biology/psychology. These three approaches overlap with each other but still have quite different goals that eventually show up in the results obtained. ” In evolutionary robotics, the predominant class of systems for generating behaviors is Artificial Neural Networks (ANNs) [Hus98]. ANNs can be envisaged as simple nodes connected together by directional wires along which signals flow.

Right now, we are aware of a few of these deep principles. We need more. We also need a better understanding of how to use those principles. 1 Ian Stewart 1 Life’s Other Secret, John Wiley & Sons, 1998, p 247. 1 Self-Organization One of the main features that makes the multi-agent approach attractive in synthesizing the adaptive behavior of a multi-robot system is the possibility of selforganization [Nol98]. The advantages of self-organization and the efficiency of self-organizing behaviors in animal societies can be readily noted.

Using update function . Update internal state = ( a r I UIsar I F a s U The internal state encodes the information the learning algorithm saves about the world, usually in the form of a table maintaining state and action data. The update function adjusts the current state based on the received reinforcement and maps the current internal state, input, action, and reinforcement into a new internal state. The evaluation function maps an internal state and an input into an action based on the information stored in the internal state.

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Multiagent Robotic Systems by Jiming Liu


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