3 citations · 12 across the 9 of their papers we have counts for
10 papers
LLM-assisted Semantic Option Discovery for Facilitating Adaptive Deep Reinforcement Learning
Chang Yao, Jinghui Qin, Kebing Jin +1
Despite achieving remarkable success in complex tasks, Deep Reinforcement Learning (DRL) is still suffering from critical issues in practical applications, such as low data efficie…
Planning with Logical Graph-based Language Model for Instruction Generation
Fan Zhang, Kebing Jin, Hankz Hankui Zhuo
Despite the superior performance of large language models to generate natural language texts, it is hard to generate texts with correct logic according to a given task, due to the…
Sequential Condition Evolved Interaction Knowledge Graph for Traditional Chinese Medicine Recommendation
Jingjin Liu, Hankz Hankui Zhuo, Kebing Jin +3
Traditional Chinese Medicine (TCM) has a rich history of utilizing natural herbs to treat a diversity of illnesses. In practice, TCM diagnosis and treatment are highly personalized…
A Hierarchical Temporal Planning-Based Approach for Dynamic Hoist Scheduling Problems
Kebing Jin, Yingkai Xiao, Hankz Hankui Zhuo +1
Hoist scheduling has become a bottleneck in electroplating industry applications with the development of autonomous devices. Although there are a few approaches proposed to target…
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…
Text-Based Action-Model Acquisition for Planning
Kebing Jin, Huaixun Chen, Hankz Hankui Zhuo
Although there have been approaches that are capable of learning action models from plan traces, there is no work on learning action models from textual observations, which is perv…
Integrating AI Planning with Natural Language Processing: A Combination of Explicit and Tacit Knowledge
Kebing Jin, Hankz Hankui Zhuo
Natural language processing (NLP) aims at investigating the interactions between agents and humans, processing and analyzing large amounts of natural language data. Large-scale lan…
Creativity of AI: Hierarchical Planning Model Learning for Facilitating Deep Reinforcement Learning
Hankz Hankui Zhuo, Shuting Deng, Mu Jin +4
Despite of achieving great success in real-world applications, Deep Reinforcement Learning (DRL) is still suffering from three critical issues, i.e., data efficiency, lack of the i…
Retrosynthetic Planning with Experience-Guided Monte Carlo Tree Search
Siqi Hong, Hankz Hankui Zhuo, Kebing Jin +2
In retrosynthetic planning, the huge number of possible routes to synthesize a complex molecule using simple building blocks leads to a combinatorial explosion of possibilities. Ev…
Gradient-Based Mixed Planning with Symbolic and Numeric Action Parameters
Kebing Jin, Hankz Hankui Zhuo, Zhanhao Xiao +2
Dealing with planning problems with both logical relations and numeric changes in real-world dynamic environments is challenging. Existing numeric planning systems for the problem…