22 citations · 28 across the 13 of their papers we have counts for
7 papers · 1 filter
A2DEPT: Large Language Model-Driven Automated Algorithm Design via Evolutionary Program Trees
Bin Chen, Shouliang Zhu, Beidan Liu +4
Designing heuristics for combinatorial optimization problems (COPs) is a fundamental yet challenging task that traditionally requires extensive domain expertise. Recently, Large La…
A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue
Wenhao Lu, Zhengqiu Zhu, Yong Zhao +5
Mobile crowdsensing is evolving beyond traditional human-centric models by integrating heterogeneous entities like unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGV…
AutoSearch: Unlocking the Reasoning Potential of Large Models for Web-based Source Search
Zhengqiu Zhu, Yatai Ji, Jiaheng Huang +3
Web-based management systems have been widely used in risk control and industrial safety. However, effectively integrating source search capabilities into these systems, to enable…
CityEQA: A Hierarchical LLM Agent on Embodied Question Answering Benchmark in City Space
Yong Zhao, Kai Xu, Zhengqiu Zhu +7
Embodied Question Answering (EQA) has primarily focused on indoor environments, leaving the complexities of urban settings-spanning environment, action, and perception-largely unex…
A Data-driven Crowd Simulation Framework Integrating Physics-informed Machine Learning with Navigation Potential Fields
Runkang Guo, Bin Chen, Qi Zhang +3
Traditional rule-based physical models are limited by their reliance on singular physical formulas and parameters, making it difficult to effectively tackle the intricate tasks ass…
Heterogeneous Graph Reinforcement Learning for Dependency-aware Multi-task Allocation in Spatial Crowdsourcing
Yong Zhao, Zhengqiu Zhu, Chen Gao +3
Spatial Crowdsourcing (SC) is gaining traction in both academia and industry, with tasks on SC platforms becoming increasingly complex and requiring collaboration among workers wit…
Conversational Crowdsensing: A Parallel Intelligence Powered Novel Sensing Approach
Zhengqiu Zhu, Yong Zhao, Bin Chen +6
The transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 brings new requirements and opportunities to current sensing approaches, especially in light of recent progres…