26 citations · 56 across the 7 of their papers we have counts for
13 papers
Learning Visual Planning Models from Partially Observed Images
Kebing Jin, Zhanhao Xiao, Hankui Hankz Zhuo +2
There has been increasing attention on planning model learning in classical planning. Most existing approaches, however, focus on learning planning models from structured data in s…
Learning Symbolic Rules for Interpretable Deep Reinforcement Learning
Zhihao Ma, Yuzheng Zhuang, Paul Weng +4
Recent progress in deep reinforcement learning (DRL) can be largely attributed to the use of neural networks. However, this black-box approach fails to explain the learned policy i…
Dual Graph Representation Learning
Huiling Zhu, Xin Luo, Hankz Hankui Zhuo
Graph representation learning embeds nodes in large graphs as low-dimensional vectors and is of great benefit to many downstream applications. Most embedding frameworks, however, a…
Refining HTN Methods via Task Insertion with Preferences
Zhanhao Xiao, Hai Wan, Hankui Hankz Zhuo +3
Hierarchical Task Network (HTN) planning is showing its power in real-world planning. Although domain experts have partial hierarchical domain knowledge, it is time-consuming to sp…
Learning Action Models from Disordered and Noisy Plan Traces
Hankz Hankui Zhuo, Jing Peng, Subbarao Kambhampati
There is increasing awareness in the planning community that the burden of specifying complete domain models is too high, which impedes the applicability of planning technology in…
Representation Learning for Classical Planning from Partially Observed Traces
Zhanhao Xiao, Hai Wan, Hankui Hankz Zhuo +2
Specifying a complete domain model is time-consuming, which has been a bottleneck of AI planning technique application in many real-world scenarios. Most classical domain-model lea…