Beyond Simple Rule Extraction: Acquiring Planning Knowledge from Neural Networks

摘要：

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 subsyrnbolic 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 subsymbobc to symbolic transition in an integrated autonomous learning framework.

作者: Ron Sun

作者单位: CECS Department, University of Missouri Columbia, MO, USA

会议类型: 国际会议

会议名称: 8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)

会议地点: 上海

会议语种:英文

页码: 177-180

在线出版日期: 2001-11-14（万方平台首次上网日期，不代表论文的发表时间）