activity
20192022
most citedRepresentation Learning for Classical Planning from Partially Observed Traces

3 citations · 5 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2022

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…

cs.CL2022

TRAC: A Textual Benchmark for Reasoning about Actions and Change

Weinan He, Canming Huang, Zhanhao Xiao +1

Reasoning about actions and change (RAC) is essential to understand and interact with the ever-changing environment. Previous AI research has shown the importance of fundamental an…

cs.SE2021

Structural Similarity of Boundary Conditions and an Efficient Local Search Algorithm for Goal Conflict Identification

Hongzhen Zhong, Hai Wan, Weilin Luo +3

In goal-oriented requirements engineering, goal conflict identification is of fundamental importance for requirements analysis. The task aims to find the feasible situations which…

cs.AI20192 cited

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…

cs.AI20193 cited

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…