Mapping the Growing Neural Gas to Situation Calculus
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Knowledge extraction from local function networks
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
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This paper will discuss learning in hybrid models that goes beyond simple rule extraction from backpropagation networks. Although simple rule extraction has received a lot of research attention, to further develop hybrid-learning models that include both symbolic and sub-symbolic knowledge and that learn autonomously, it is necessary to study autonomous learning of both sub-symbolic and symbolic knowledge in integrated architectures. This paper will describe knowledge extraction from neural reinforcement learning. It includes two approaches to wards extracting plan knowledge: the extraction of explicit, symbolic rules from neural reinforcement learning, and the extraction of complete plans. This work points to the creation of a general framework for achieving the sub-symbolic to symbolic transition in an integrated autonomous learning framework.