Abstract :
Artificial neural networks can model cortical local learning and signal processing, but they are not the brain, neither are many special purpose systems to which they contribute. Autonomous mental development models all or part of the brain (or the central nervous system) and how it develops and learns autonomously from infancy to adulthood. Like neural network research, such modeling aims to be biologically plausible. This paper discusses why autonomous development is necessary according to a concept called task muddiness. Then it introduces results for a series of research issues, including the new paradigm for autonomous development, mental architectures, developmental algorithm, a refined classification of types of machine learning, spatial complexity and time complexity. Finally, the paper presents some experimental results for applications, including: vision-guided navigation, object finding, object-based attention (eye-pan), and attention-guided pre-reaching, tour tasks that infants learn to perform early but very perceptually challenging for robots
Keywords :
computational complexity; learning (artificial intelligence); neural nets; autonomous mental development; machine learning; neural networks; spatial complexity; task muddiness; time complexity; Artificial neural networks; Autonomous mental development; Biological neural networks; Biological system modeling; Biomedical signal processing; Brain modeling; Central nervous system; Classification algorithms; Machine learning algorithms; Signal processing algorithms;
