Abstract
This paper presents a new method for developmental robot based on a learning automaton. This method can be considered as active learning to select the best action in order to quickly adapt environment. The new model built in the framework of learning automata theory is an abstract and formal mathematical tool to describe the cognitive behavior or cognitive development mechanisms and provide an effective logical structure for the design of cognitive and development robots. The model reflects the design principles of cognitive development robotics. The direction of cognition and development is towards entropy minimization and simulation results verify its effectiveness.
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Ruan, X., Dai, L., Yang, G., Chen, J. (2011). A Developmental Learning Based on Learning Automata. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6676. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21090-7_67
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DOI: https://doi.org/10.1007/978-3-642-21090-7_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21089-1
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