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Fitness, environment and input: Evolved robotic shepherding

By M. Henry Linder and Ben Nye


A simple shepherding task is used as a framework in which to investigate the role played by fitness functions, environmental design and inputs to a neural network in the evolution of robotic behavior. While a cursory approach to this experimental problem might suggest that the only factor relevant to success is the function used to gauge a robotic brain’s fitness, experimental results indicate that the structure of the robot’s environment, as well as the sensory input provided to the robot about both the physical environment as well as task-specific environmental features, play an equally important role in a robots success (or unsuccess). Furthermore, previous research indicates that the fitness function used must be designed carefully, in order to encourage further development of desired behavior by properly rewarding successful behavior and emphasizing relevant measures of success; it is essential to give constructive rewards to effectively indicate the underlying task, which can be difficult given a human interpretation of the domain under consideration. Furthermore, previous experiments involving robotic shepherding indicate that the evolution of desired behavior is not only possible but nearly trivial; in this experiment, we examine why our previous experiments in this field met with unsuccess, and discuss how our updated experimental method induced success.

Year: 2010
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