Abstract
This paper discusses learning in hybrid models that goes beyond simple classification rule extraction from backpropagation networks. Although simple rule extraction has received a lot of research attention, we need to further develop hybrid learning models that learn autonomously and acquire both symbolic and subsymbolic knowledge. It is also necessary to study autonomous learning of both subsymbolic and symbolic knowledge in integrated architectures. This paper will describe planning knowledge extraction from neural reinforcement learning that goes beyond extracting simple rules. It includes two approaches towards extracting planning knowledge: the extraction of symbolic rules from neural reinforcement learning, and the extraction of complete plans. This work points to a general framework for achieving the subsymbolic to symbolic transition in an integrated autonomous learning framework.
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Sun, R., Peterson, T., Sessions, C. (2002). Beyond Simple Rule Extraction: Acquiring Planning Knowledge from Neural Networks. In: Tagliaferri, R., Marinaro, M. (eds) Neural Nets WIRN Vietri-01. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0219-9_32
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DOI: https://doi.org/10.1007/978-1-4471-0219-9_32
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