no code implementations • 25 Nov 2022 • Weinan He, Canming Huang, Zhanhao Xiao, Yongmei Liu
Reasoning about actions and change (RAC) is essential to understand and interact with the ever-changing environment.
no code implementations • 25 Nov 2022 • Kebing Jin, Zhanhao Xiao, Hankui Hankz Zhuo, Hai Wan, Jiaran Cai
Although a number of approaches have been developed for learning planning models from fully observed unstructured data (e. g., images), in many scenarios raw observations are often incomplete.
no code implementations • 19 Oct 2021 • Kebing Jin, Hankz Hankui Zhuo, Zhanhao Xiao, Hai Wan, Subbarao Kambhampati
In this paper, we propose a novel algorithm framework to solve numeric planning problems mixed with logical relations and numeric changes based on gradient descent.
no code implementations • 23 Feb 2021 • Hongzhen Zhong, Hai Wan, Weilin Luo, Zhanhao Xiao, Jia Li, Biqing Fang
By taking experiments on a set of cases, we show that LOGION effectively exploits the structural similarity of BCs.
no code implementations • 29 Nov 2019 • Zhanhao Xiao, Hai Wan, Hankui Hankz Zhuo, Andreas Herzig, Laurent Perrussel, Peilin Chen
Hierarchical Task Network (HTN) planning is showing its power in real-world planning.
no code implementations • 19 Jul 2019 • Zhanhao Xiao, Hai Wan, Hankui Hankz Zhuo, Jinxia Lin, Yanan Liu
The experimental results show that the domain models learned by our approach are much more effective on solving real planning problems.