Memetic compact differential evolution for cartesian robot control
IEEE Computational Intelligence Magazine
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This article provides a brief introduction to hierarchical discriminant regression and lobe component analysis, two algorithms usable in components of the mental architecture of an AMD system. The processes that guide the development of the "mind" of an autonomously-learning robot must be given within the developmental program. This is what must drive the learning of the robot's eventual behaviors and skills, which emerges in response to the robot's interactions with the environment. A biologically sophisticated version of a developmental program, where behaviors and skills autonomously emerge in the robot with a human level of complexity, has not yet been written. But there has been progress in creating developmental "building blocks." These are designed to be used in components of the developmental program. These algorithms must be specially designed to handle many of the difficult constraints of AMD. In this article, we introduce two such algorithms, briefly outline their purpose and key features, and describe how to use sample versions available on the El lab's Web site