Solving Large-Scale Planning Problems by Decomposition and Macro Generation

Large-scale classical planning problems such as factory as- sembly problems pose a significant challenge for domain- independent planners. We propose a macro-based planner which automatically identifies subproblems, generate macros from subplans and integrate the subplans by solving the whole augmented problem. We show experimentally that our approach can be used to solve large problem instances that are beyond the reach of current state-of-the-art satisficing plan- ners

最良優先探索のための探索非局在化手法

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 Fukunaga Alex, 東京大学教授 山口 和紀, 東京大学准教授 田中 哲朗, 東京大学准教授 金子 知適, 東京大学准教授 森畑 明昌University of Tokyo(東京大学