26 citations · 97 across the 21 of their papers we have counts for
17 papers · 1 filter
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…
Inspire or Predict? Exploring New Paradigms in Assisting Classical Planners with Large Language Models
Wenkai Yu, Jianhang Tang, Yang Zhang +4
Addressing large-scale planning problems has become one of the central challenges in the planning community, deriving from the state-space explosion caused by growing objects and a…
BalMCTS: Balancing Objective Function and Search Nodes in MCTS for Constraint Optimization Problems
Yingkai Xiao, Jingjin Liu, Hankz Hankui Zhuo
Constraint Optimization Problems (COP) pose intricate challenges in combinatorial problems usually addressed through Branch and Bound (B\&B) methods, which involve maintaining prio…
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…
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…
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…
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…
An Integrated Development Environment for Planning Domain Modeling
Yuncong Li, Hankz Hankui Zhuo
In order to make the task, description of planning domains and problems, more comprehensive for non-experts in planning, the visual representation has been used in planning domain…
Discovering Underlying Plans Based on Shallow Models
Hankz Hankui Zhuo, Yantian Zha, Subbarao Kambhampati
Plan recognition aims to discover target plans (i.e., sequences of actions) behind observed actions, with history plan libraries or domain models in hand. Previous approaches eithe…
Extracting Action Sequences from Texts Based on Deep Reinforcement Learning
Wenfeng Feng, Hankz Hankui Zhuo, Subbarao Kambhampati
Extracting action sequences from natural language texts is challenging, as it requires commonsense inferences based on world knowledge. Although there has been work on extracting a…
Embedding Knowledge Graphs Based on Transitivity and Antisymmetry of Rules
Mengya Wang, Hankui Zhuo, Huiling Zhu
Representation learning of knowledge graphs encodes entities and relation types into a continuous low-dimensional vector space, learns embeddings of entities and relation types. Mo…
Discovering Underlying Plans Based on Distributed Representations of Actions
Xin Tian, Hankz Hankui Zhuo, Subbarao Kambhampati
Plan recognition aims to discover target plans (i.e., sequences of actions) behind observed actions, with history plan libraries or domain models in hand. Previous approaches eithe…
Herding the Crowd: Automated Planning for Crowdsourced Planning
Kartik Talamadupula, Subbarao Kambhampati
There has been significant interest in crowdsourcing and human computation. One subclass of human computation applications are those directed at tasks that involve planning (e.g. t…
Model-Lite Case-Based Planning
Hankz Hankui Zhuo, Subbarao Kambhampati, Tuan Nguyen
There is increasing awareness in the planning community that depending on complete models impedes the applicability of planning technology in many real world domains where the burd…