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학술대회 An Application of LSTM Recurrent Networks to Expanding of Search Tree Nodes in Symbolic Planning
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저자
조준면, 손영성, 박준희, 박찬원
발행일
202010
출처
International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1293-1295
DOI
https://dx.doi.org/10.1109/ICTC49870.2020.9289296
협약과제
20ZR1100, 자율적으로 연결·제어·진화하는 초연결 지능화 기술 연구, 박준희
초록
Applying deep learning to symbolic planning is not straightforward because they differently internalize knowledge. One represents knowledge numerically while the other does symbolically. Hybrid methods that take strengths from both techniques will deliver better performance on automated planning. This paper presents a method to improve the performance of symbolic planning by applying LSTM recurrent networks to inferring the guidance knowledge for search tree node expansion. The networks learn sequential patterns across previous node expansions and generalizes the learned knowledge to predict promising actions and their probability scores at the present expansion step. The prediction consists with node cost estimation for heuristic search. A simple experiment vindicates the method of this research.
키워드
automated planning, deep learning, LSTM, recurrent networks, search tree
KSP 제안 키워드
Automated Planning, Recurrent network, cost estimation, deep learning(DL), heuristic search, hybrid method, search tree, sequential pattern